In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We proceed then, by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.
MIXTURES OF TRAINED REGRESSION CURVESMODELS FOR HANDRITTEN ARABIC CHARACTER R...ijaia
In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We proceed then, by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.
This document discusses and compares different methods for solving assignment problems. It begins with an abstract that defines assignment problems as optimally assigning n objects to m other objects in an injective (one-to-one) fashion. It then provides an introduction to the Hungarian method and a new proposed Matrix Ones Assignment (MOA) method. The body of the document provides details on modeling assignment problems with cost matrices, formulations as linear programs, and step-by-step explanations of the Hungarian and MOA methods. It includes an example solved using the Hungarian method.
The document provides an overview of the Foundation Course for the Actuarial Common Entrance Test (ACET). It covers 8 chapters on mathematical topics including notation, numerical methods, functions, algebra, calculus, and vectors/matrices. It recommends reviewing areas of weakness and provides additional practice questions. When studying core technical subjects, the Foundation Course can be used as a reference for mathematical concepts requiring review.
- This document outlines the syllabus for an Engineering Mathematics I course for first year mechanical power and energy engineering students.
- The course aims to provide students with a basic foundation in calculus, algebra, and numerical analysis that can be applied to mechanical power and energy engineering problems.
- Over the course, students will review single-variable calculus concepts and be introduced to numerical techniques like solving linear systems, iterative matrix methods, and solving ordinary differential equations.
Linear Regression Analysis | Linear Regression in Python | Machine Learning A...Simplilearn
This Linear Regression in Machine Learning Presentation will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning presentation is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms.
Below topics are covered in this Linear Regression Machine Learning Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Use case - Profit estimation of companies
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
- - - - - -
This document discusses various types of regression modeling and linear regression. It provides examples of linear regression analysis on fraud data and discusses assessing goodness of fit. It also briefly covers non-linear regression, problem areas like heteroskedasticity and collinearity, and model selection methods. Linear regression is presented geometrically and the assumptions and computations of ordinary least squares regression are explained.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
1. Graphical analysis is a powerful tool for determining relationships between experimental variables. Key is plotting variables such that the relationship appears as a straight line.
2. Linear relationships yield straight lines with slope equaling m and intercept b. Nonlinear relationships may require log-log or semilog plots.
3. Sources of error include measurement uncertainties. Propagating these through calculations yields final result uncertainties.
MIXTURES OF TRAINED REGRESSION CURVESMODELS FOR HANDRITTEN ARABIC CHARACTER R...ijaia
In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We proceed then, by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.
This document discusses and compares different methods for solving assignment problems. It begins with an abstract that defines assignment problems as optimally assigning n objects to m other objects in an injective (one-to-one) fashion. It then provides an introduction to the Hungarian method and a new proposed Matrix Ones Assignment (MOA) method. The body of the document provides details on modeling assignment problems with cost matrices, formulations as linear programs, and step-by-step explanations of the Hungarian and MOA methods. It includes an example solved using the Hungarian method.
The document provides an overview of the Foundation Course for the Actuarial Common Entrance Test (ACET). It covers 8 chapters on mathematical topics including notation, numerical methods, functions, algebra, calculus, and vectors/matrices. It recommends reviewing areas of weakness and provides additional practice questions. When studying core technical subjects, the Foundation Course can be used as a reference for mathematical concepts requiring review.
- This document outlines the syllabus for an Engineering Mathematics I course for first year mechanical power and energy engineering students.
- The course aims to provide students with a basic foundation in calculus, algebra, and numerical analysis that can be applied to mechanical power and energy engineering problems.
- Over the course, students will review single-variable calculus concepts and be introduced to numerical techniques like solving linear systems, iterative matrix methods, and solving ordinary differential equations.
Linear Regression Analysis | Linear Regression in Python | Machine Learning A...Simplilearn
This Linear Regression in Machine Learning Presentation will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning presentation is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms.
Below topics are covered in this Linear Regression Machine Learning Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Use case - Profit estimation of companies
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
- - - - - -
This document discusses various types of regression modeling and linear regression. It provides examples of linear regression analysis on fraud data and discusses assessing goodness of fit. It also briefly covers non-linear regression, problem areas like heteroskedasticity and collinearity, and model selection methods. Linear regression is presented geometrically and the assumptions and computations of ordinary least squares regression are explained.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
1. Graphical analysis is a powerful tool for determining relationships between experimental variables. Key is plotting variables such that the relationship appears as a straight line.
2. Linear relationships yield straight lines with slope equaling m and intercept b. Nonlinear relationships may require log-log or semilog plots.
3. Sources of error include measurement uncertainties. Propagating these through calculations yields final result uncertainties.
This document provides an overview of regression models and their use in business analytics. It discusses simple and multiple linear regression models, how to develop regression equations from sample data, and how to interpret key outputs like the slope, intercept, coefficient of determination, and correlation coefficient. Regression analysis is presented as a valuable tool for managers to understand relationships between variables and predict outcomes. The document outlines the key steps in regression including developing scatter plots, calculating regression equations, and measuring the fit of regression models.
This document discusses regression analysis and linear regression models. It provides examples of how regression can be used to understand relationships between variables and predict values. Specifically, it examines a case study of how sales from a home renovation company (Triple A Construction) can be predicted based on area payroll. The regression line that best fits the data is calculated and metrics like the coefficient of determination are used to evaluate how well the regression model fits the data. Assumptions of the linear regression model like independent and normally distributed errors are also covered.
The document discusses various techniques for curve fitting data, including interpolation, linear regression, and higher-order polynomial fitting. It begins by explaining the motivation for curve fitting as creating a single function to represent trends in observed data. Linear regression finds the best-fit straight line by minimizing the squared errors between data points and the line. Higher-order polynomials allow fitting nonlinear trends by finding coefficients for polynomial functions up to a given order, such as quadratic, that also minimize the squared errors.
A quick introduction to linear and logistic regression using Python. Part of the Data Science Bootcamp held in Amman by the Jordan Open Source Association Dec/Jan 2015. Reference code can be found on Github https://github.com/jordanopensource/data-science-bootcamp/tree/master/MachineLearning/Session1
This document discusses matrix addition and subtraction. It states that two matrices are equal if they are the same size and have equal corresponding elements. The sum of two matrices is a matrix with elements that are the sums of the corresponding elements. Addition is commutative and associative for matrices of the same size. A zero matrix has all elements equal to zero. The negative of a matrix has elements that are the negatives of the original matrix's elements.
This document presents a presentation on regression analysis submitted to Dr. Adeel. It includes:
- An introduction to regression analysis and its uses in measuring relationships between variables and making predictions.
- Methods for studying regression including graphically, algebraically using least squares, and deviations from means.
- An example calculating regression equations using data on students' grades and scores through least squares and deviations from means.
- Conclusion that the regression equations match those obtained through other common methods.
Math 1300: Section 5-1 Inequalities in Two VariablesJason Aubrey
This document discusses how to graph linear inequalities in two variables. It begins by introducing linear equations and noting that linear inequalities need to be graphed differently. Key terminology for half-planes divided by boundary lines is provided. The procedure is then outlined as graphing the boundary line and testing a point to determine which half-plane satisfies the inequality. An example of graphing the inequality y ≤ x - 1 is worked through step-by-step.
Math 1300: Section 4-5 Inverse of a Square MatrixJason Aubrey
This document is a lecture on identity matrices given by Jason Aubrey of the University of Missouri Department of Mathematics. It defines an identity matrix as an n×n matrix with 1s on the main diagonal and 0s elsewhere. Examples of 2×2 and 3×3 identity matrices are given. The key property that the product of a matrix and the identity matrix equals the original matrix is also described. An example calculation of multiplying two matrices is shown step-by-step to illustrate the use of the identity matrix.
11.5 point slope form of a linear equationGlenSchlee
The document discusses writing equations of lines using different forms. It explains how to use point-slope form to write an equation of a line given a point and slope. It also shows how to write an equation of a line using two points on the line. Finally, it demonstrates how to write an equation that models a linear data set by plotting the data points and using two points and the point-slope form.
This document provides an overview of key concepts in regression analysis, including simple and multiple linear regression models. It outlines 10 learning objectives for the chapter, which cover topics like developing regression equations from sample data, interpreting regression outputs, assessing model fit, and addressing violations of regression assumptions. The document also includes sample regression calculations and residual plots for a case study on predicting home renovation sales from area payroll levels.
Polynomial regression models the relationship between variables as a polynomial equation rather than a linear one. It allows for modeling of curvilinear relationships. The document discusses the definition of polynomial regression, why it is used, its history, the regression model and matrix form, how to implement it in Matlab, its advantages in fitting flexible curves, and its disadvantages related to sensitivity to outliers.
Estimation of Spring Stiffness Under Conditions of Uncertainty. Interval Appr...atsidaev
This document describes a method for estimating the stiffness of a spring under conditions of uncertainty, using an interval analysis approach. Key points:
- Interval analysis methods are applied to estimate spring stiffness parameters using a small dataset of force and compression measurements corrupted by unknown errors.
- An information set of possible stiffness and initial length parameters is constructed based on the measurement data and uncertainty bounds.
- A tube of admissible dependency curves is computed based on the information set, providing a guaranteed bounded range for the spring relationship.
- The interval analysis approach is shown to provide better estimates than standard statistical methods, given the limited data and lack of probability information on errors.
Regeneration of simple and complicated curves using Fourier seriesIJAEMSJORNAL
This document describes using Fourier series to regenerate simple and complex curves composed of sine waves. Fourier series is shown to be an effective method for this purpose. As an example, a periodic sine wave pattern with 22 data points is modeled using a Fourier series with 4 harmonics. The Fourier coefficients are estimated using a statistical software package. The estimated Fourier model is then used to regenerate the sine wave pattern with good accuracy compared to the original data points. The document demonstrates how Fourier series can reliably regenerate periodic patterns constructed from sine curves.
This presentation is aimed at fitting a Simple Linear Regression model in a Python program. IDE used is Spyder. Screenshots from a working example are used for demonstration.
This presentation provides an introduction to differential calculus. It defines calculus and differentiation, and classifies calculus into differential calculus and integral calculus. Differential calculus deals with finding rates of change of functions with respect to variables using derivatives, while integral calculus involves determining lengths, areas, volumes, and solving differential equations using integrals. The presentation explains key calculus concepts like derivatives, differentiation, and differential curves. It concludes by presenting some common formulas for differentiation.
This chapter discusses simple linear regression analysis. It explains that regression analysis is used to predict the value of a dependent variable based on the value of at least one independent variable. The chapter outlines the simple linear regression model, which involves one independent variable and attempts to describe the relationship between the dependent and independent variables using a linear function. It provides examples to demonstrate how to obtain and interpret the regression equation and coefficients based on sample data. Key outputs from regression analysis like measures of variation, the coefficient of determination, and tests of significance are also introduced.
Comparing the methods of Estimation of Three-Parameter Weibull distributionIOSRJM
This document compares different methods for estimating the parameters of a three-parameter Weibull distribution from sample data, including graphical methods, trial and error, Jiang/Murthy's approach, and maximum likelihood estimation. It applies these methods to simulated sample data from a Weibull distribution with known parameters to estimate the location, scale, and shape parameters. The trial and error method uses residual sum of squares to select the location parameter that provides the best fit. The maximum likelihood method derives equations that are solved numerically. The results show that all methods can estimate the parameters reasonably well, though accuracy varies depending on the full or censored sample size.
This document provides an overview of the Maple computer algebra system. It discusses that Maple is a powerful commercial program for doing mathematical computations symbolically and numerically. It describes the different interfaces of Maple including worksheet mode, document mode, and others. The document also outlines basic Maple syntax and commands for performing calculations, solving equations, plotting functions, and more. It provides information on system requirements and concludes that Maple is a useful tool for engaging in mathematical learning.
This document provides an overview of regression analysis. It defines regression analysis as a predictive modeling technique used to investigate relationships between dependent and independent variables. It describes simple linear regression as involving one independent variable and one dependent variable, with the goal of finding the best fitting straight line through the data points. An example is provided to demonstrate how to conduct a simple linear regression to predict population in the year 2005 based on population data from previous years.
Abstract: This PDSG workshop introduces basic concepts of multiple linear regression in machine learning. Concepts covered are Feature Elimination and Backward Elimination, with examples in Python.
Level: Fundamental
Requirements: Should have some experience with Python programming.
LEARNING SPLINE MODELS WITH THE EM ALGORITHM FOR SHAPE RECOGNITIONgerogepatton
This paper demonstrates how cubic Spline (B-Spline) models can be used to recognize 2-dimension nonrigid handwritten isolated characters. Each handwritten character is represented by a set of nonoverlapping uniformly distributed landmarks. The Spline models are constructed by utilizing cubic order of
polynomial to model the shapes under study. The approach is a two-stage process. The first stage is
learning, we construct a mixture of spline class parameters to capture the variations in spline coefficients
using the apparatus Expectation Maximization algorithm. The second stage is recognition, here we use the
Fréchet distance to compute the variations between the spline models and test spline shape for recognition.
We test the approach on a set of handwritten Arabic letters.
Applied Mathematics and Sciences: An International Journal (MathSJ)mathsjournal
The main goal of this research is to give the complete conception about numerical integration including
Newton-Cotes formulas and aimed at comparing the rate of performance or the rate of accuracy of
Trapezoidal, Simpson’s 1/3, and Simpson’s 3/8. To verify the accuracy, we compare each rules
demonstrating the smallest error values among them. The software package MATLAB R2013a is applied to
determine the best method, as well as the results, are compared. It includes graphical comparisons
mentioning these methods graphically. After all, it is then emphasized that the among methods considered,
Simpson’s 1/3 is more effective and accurate when the condition of the subdivision is only even for solving
a definite integral.
This document provides an overview of regression models and their use in business analytics. It discusses simple and multiple linear regression models, how to develop regression equations from sample data, and how to interpret key outputs like the slope, intercept, coefficient of determination, and correlation coefficient. Regression analysis is presented as a valuable tool for managers to understand relationships between variables and predict outcomes. The document outlines the key steps in regression including developing scatter plots, calculating regression equations, and measuring the fit of regression models.
This document discusses regression analysis and linear regression models. It provides examples of how regression can be used to understand relationships between variables and predict values. Specifically, it examines a case study of how sales from a home renovation company (Triple A Construction) can be predicted based on area payroll. The regression line that best fits the data is calculated and metrics like the coefficient of determination are used to evaluate how well the regression model fits the data. Assumptions of the linear regression model like independent and normally distributed errors are also covered.
The document discusses various techniques for curve fitting data, including interpolation, linear regression, and higher-order polynomial fitting. It begins by explaining the motivation for curve fitting as creating a single function to represent trends in observed data. Linear regression finds the best-fit straight line by minimizing the squared errors between data points and the line. Higher-order polynomials allow fitting nonlinear trends by finding coefficients for polynomial functions up to a given order, such as quadratic, that also minimize the squared errors.
A quick introduction to linear and logistic regression using Python. Part of the Data Science Bootcamp held in Amman by the Jordan Open Source Association Dec/Jan 2015. Reference code can be found on Github https://github.com/jordanopensource/data-science-bootcamp/tree/master/MachineLearning/Session1
This document discusses matrix addition and subtraction. It states that two matrices are equal if they are the same size and have equal corresponding elements. The sum of two matrices is a matrix with elements that are the sums of the corresponding elements. Addition is commutative and associative for matrices of the same size. A zero matrix has all elements equal to zero. The negative of a matrix has elements that are the negatives of the original matrix's elements.
This document presents a presentation on regression analysis submitted to Dr. Adeel. It includes:
- An introduction to regression analysis and its uses in measuring relationships between variables and making predictions.
- Methods for studying regression including graphically, algebraically using least squares, and deviations from means.
- An example calculating regression equations using data on students' grades and scores through least squares and deviations from means.
- Conclusion that the regression equations match those obtained through other common methods.
Math 1300: Section 5-1 Inequalities in Two VariablesJason Aubrey
This document discusses how to graph linear inequalities in two variables. It begins by introducing linear equations and noting that linear inequalities need to be graphed differently. Key terminology for half-planes divided by boundary lines is provided. The procedure is then outlined as graphing the boundary line and testing a point to determine which half-plane satisfies the inequality. An example of graphing the inequality y ≤ x - 1 is worked through step-by-step.
Math 1300: Section 4-5 Inverse of a Square MatrixJason Aubrey
This document is a lecture on identity matrices given by Jason Aubrey of the University of Missouri Department of Mathematics. It defines an identity matrix as an n×n matrix with 1s on the main diagonal and 0s elsewhere. Examples of 2×2 and 3×3 identity matrices are given. The key property that the product of a matrix and the identity matrix equals the original matrix is also described. An example calculation of multiplying two matrices is shown step-by-step to illustrate the use of the identity matrix.
11.5 point slope form of a linear equationGlenSchlee
The document discusses writing equations of lines using different forms. It explains how to use point-slope form to write an equation of a line given a point and slope. It also shows how to write an equation of a line using two points on the line. Finally, it demonstrates how to write an equation that models a linear data set by plotting the data points and using two points and the point-slope form.
This document provides an overview of key concepts in regression analysis, including simple and multiple linear regression models. It outlines 10 learning objectives for the chapter, which cover topics like developing regression equations from sample data, interpreting regression outputs, assessing model fit, and addressing violations of regression assumptions. The document also includes sample regression calculations and residual plots for a case study on predicting home renovation sales from area payroll levels.
Polynomial regression models the relationship between variables as a polynomial equation rather than a linear one. It allows for modeling of curvilinear relationships. The document discusses the definition of polynomial regression, why it is used, its history, the regression model and matrix form, how to implement it in Matlab, its advantages in fitting flexible curves, and its disadvantages related to sensitivity to outliers.
Estimation of Spring Stiffness Under Conditions of Uncertainty. Interval Appr...atsidaev
This document describes a method for estimating the stiffness of a spring under conditions of uncertainty, using an interval analysis approach. Key points:
- Interval analysis methods are applied to estimate spring stiffness parameters using a small dataset of force and compression measurements corrupted by unknown errors.
- An information set of possible stiffness and initial length parameters is constructed based on the measurement data and uncertainty bounds.
- A tube of admissible dependency curves is computed based on the information set, providing a guaranteed bounded range for the spring relationship.
- The interval analysis approach is shown to provide better estimates than standard statistical methods, given the limited data and lack of probability information on errors.
Regeneration of simple and complicated curves using Fourier seriesIJAEMSJORNAL
This document describes using Fourier series to regenerate simple and complex curves composed of sine waves. Fourier series is shown to be an effective method for this purpose. As an example, a periodic sine wave pattern with 22 data points is modeled using a Fourier series with 4 harmonics. The Fourier coefficients are estimated using a statistical software package. The estimated Fourier model is then used to regenerate the sine wave pattern with good accuracy compared to the original data points. The document demonstrates how Fourier series can reliably regenerate periodic patterns constructed from sine curves.
This presentation is aimed at fitting a Simple Linear Regression model in a Python program. IDE used is Spyder. Screenshots from a working example are used for demonstration.
This presentation provides an introduction to differential calculus. It defines calculus and differentiation, and classifies calculus into differential calculus and integral calculus. Differential calculus deals with finding rates of change of functions with respect to variables using derivatives, while integral calculus involves determining lengths, areas, volumes, and solving differential equations using integrals. The presentation explains key calculus concepts like derivatives, differentiation, and differential curves. It concludes by presenting some common formulas for differentiation.
This chapter discusses simple linear regression analysis. It explains that regression analysis is used to predict the value of a dependent variable based on the value of at least one independent variable. The chapter outlines the simple linear regression model, which involves one independent variable and attempts to describe the relationship between the dependent and independent variables using a linear function. It provides examples to demonstrate how to obtain and interpret the regression equation and coefficients based on sample data. Key outputs from regression analysis like measures of variation, the coefficient of determination, and tests of significance are also introduced.
Comparing the methods of Estimation of Three-Parameter Weibull distributionIOSRJM
This document compares different methods for estimating the parameters of a three-parameter Weibull distribution from sample data, including graphical methods, trial and error, Jiang/Murthy's approach, and maximum likelihood estimation. It applies these methods to simulated sample data from a Weibull distribution with known parameters to estimate the location, scale, and shape parameters. The trial and error method uses residual sum of squares to select the location parameter that provides the best fit. The maximum likelihood method derives equations that are solved numerically. The results show that all methods can estimate the parameters reasonably well, though accuracy varies depending on the full or censored sample size.
This document provides an overview of the Maple computer algebra system. It discusses that Maple is a powerful commercial program for doing mathematical computations symbolically and numerically. It describes the different interfaces of Maple including worksheet mode, document mode, and others. The document also outlines basic Maple syntax and commands for performing calculations, solving equations, plotting functions, and more. It provides information on system requirements and concludes that Maple is a useful tool for engaging in mathematical learning.
This document provides an overview of regression analysis. It defines regression analysis as a predictive modeling technique used to investigate relationships between dependent and independent variables. It describes simple linear regression as involving one independent variable and one dependent variable, with the goal of finding the best fitting straight line through the data points. An example is provided to demonstrate how to conduct a simple linear regression to predict population in the year 2005 based on population data from previous years.
Abstract: This PDSG workshop introduces basic concepts of multiple linear regression in machine learning. Concepts covered are Feature Elimination and Backward Elimination, with examples in Python.
Level: Fundamental
Requirements: Should have some experience with Python programming.
LEARNING SPLINE MODELS WITH THE EM ALGORITHM FOR SHAPE RECOGNITIONgerogepatton
This paper demonstrates how cubic Spline (B-Spline) models can be used to recognize 2-dimension nonrigid handwritten isolated characters. Each handwritten character is represented by a set of nonoverlapping uniformly distributed landmarks. The Spline models are constructed by utilizing cubic order of
polynomial to model the shapes under study. The approach is a two-stage process. The first stage is
learning, we construct a mixture of spline class parameters to capture the variations in spline coefficients
using the apparatus Expectation Maximization algorithm. The second stage is recognition, here we use the
Fréchet distance to compute the variations between the spline models and test spline shape for recognition.
We test the approach on a set of handwritten Arabic letters.
Applied Mathematics and Sciences: An International Journal (MathSJ)mathsjournal
The main goal of this research is to give the complete conception about numerical integration including
Newton-Cotes formulas and aimed at comparing the rate of performance or the rate of accuracy of
Trapezoidal, Simpson’s 1/3, and Simpson’s 3/8. To verify the accuracy, we compare each rules
demonstrating the smallest error values among them. The software package MATLAB R2013a is applied to
determine the best method, as well as the results, are compared. It includes graphical comparisons
mentioning these methods graphically. After all, it is then emphasized that the among methods considered,
Simpson’s 1/3 is more effective and accurate when the condition of the subdivision is only even for solving
a definite integral.
HANDWRITTEN CHARACTER RECOGNITION USING STRUCTURAL SHAPE DECOMPOSITIONcsandit
This paper presents a statistical framework for recognising 2D shapes which are represented as
an arrangement of curves or strokes. The approach is a hierarchical one which mixes geometric
and symbolic information in a three-layer architecture. Each curve primitive is represented
using a point-distribution model which describes how its shape varies over a set of training
data. We assign stroke labels to the primitives and these indicate to which class they belong.
Shapes are decomposed into an arrangement of primitives and the global shape representation
has two components. The first of these is a second point distribution model that is used to
represent the geometric arrangement of the curve centre-points. The second component is a
string of stroke labels that represents the symbolic arrangement of strokes. Hence each shape
can be represented by a set of centre-point deformation parameters and a dictionary of
permissible stroke label configurations. The hierarchy is a two-level architecture in which the
curve models reside at the nonterminal lower level of the tree. The top level represents the curve
arrangements allowed by the dictionary of permissible stroke combinations. The aim in
recognition is to minimise the cross entropy between the probability distributions for geometric
alignment errors and curve label errors. We show how the stroke parameters, shape-alignment
parameters and stroke labels may be recovered by applying the expectation maximization EM
algorithm to the utility measure. We apply the resulting shape-recognition method to Arabic
character recognition.
LOGNORMAL ORDINARY KRIGING METAMODEL IN SIMULATION OPTIMIZATIONorajjournal
This paper presents a lognormal ordinary kriging (LOK) metamodel algorithm and its application to
optimize a stochastic simulation problem. Kriging models have been developed as an interpolation method
in geology. They have been successfully used for the deterministic simulation optimization (SO) problem. In
recent years, kriging metamodeling has attracted a growing interest with stochastic problems. SO
researchers have begun using ordinary kriging through global optimization in stochastic systems. The
goals of this study are to present LOK metamodel algorithm and to analyze the result of the application
step-by-step. The results show that LOK is a powerful alternative metamodel in simulation optimization
when the data are too skewed.
A big task often faced by practitioners is in deciding the appropriate model to adopt
in fitting count datasets. This paper is aimed at investigating a suitable model for fitting
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MIXTURES OF TRAINED REGRESSION CURVES MODELS FOR HANDWRITTEN ARABIC CHARACTER RECOGNITION
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.10, No.3, May 2019
DOI : 10.5121/ijaia.2019.10305 49
MIXTURES OF TRAINED REGRESSION CURVES
MODELS FOR HANDWRITTEN ARABIC CHARACTER
RECOGNITION
Abdullah A. Al-Shaher1
1
Department of Computer and Information Systems
College of Business Studies, Public Authority for
Applied Education and Training, Kuwait
ABSTRACT
In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten
shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The
underlying models utilize 2nd order of polynomials to model shapes within a training set. To estimate the
regression models, we need to extract the required coefficients which describe the variations for a set of
shape class. Hence, a least square method is used to estimate such modes. We proceed then, by training these
coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding
the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic
characters are used to evaluate our approach.
KEYWORDS
Shape Recognition, Arabic Handwritten Characters, Regression Curves, Expectation Maximization
Algorithm.
1.INTRODUCTION
Shape recognition has been the focus of many researchers for the past seven decades [1] and
attracted many communities in the field of pattern recognition [2], artificial intelligence[3], signal
processing [4], image analysis [5], and computer vision [6]. The difficulties arise when the shape
under study exhibits a high degree in variation: as in handwritten characters [7], digits [8], face
detection [9], and gesture authentication [10]. For a single data, shape variation is limited and
cannot be captured ultimately due to the fact that single data does not provide sufficient information
and knowledge about the data; therefore, multiple existence of data provides better understanding
of shape analysis and manifested by mixture models [11]. Because of the existence of multivariate
data under study, there is always the need to estimate the parameters which describe the data that is
encapsulated within a mixture of shapes.
The literature demonstrates many statistical and structural approaches to various algorithms to
model shape variations using supervised and unsupervised learning [12] algorithms. Precisely, the
powerful Expectation Maximization Algorithm of Dempster [13] has widely been used for such
cases. The EM Algorithm revolves around two step procedures. The expectation E step revolves
around estimating the parameters of a log-likelihood function and passes it to the Maximization M
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.10, No.3, May 2019
50
step. In a maximization (M) step, the algorithm computes parameters maximizing the expected
log-likelihood found on the E step. The process is iterative one until all parameters come to
unchanged. For instance, Jojic and Frey [14] have used the EM algorithm to fit mixture models to
the appearances manifolds for faces. Bishop and Winn [15] have used a mixture of principal
components analyzers to learn and synthesize variations in facial appearance. Vasconcelos and
Lippman [16] have used the EM Algorithm to learn queries for content-based image retrieval. In
general, several authors have used the EM algorithm to track multiple moving objects [17]. Revov
et al. [18] have developed a generative model which can be used for handwritten character
recognition. Their method employs the EM algorithm to model the distribution of sample points.
Curves are widely used in research by the computer vision society [1] [2] [3] [4] [5]. Curvatures are
mainly used to distinguish different shapes such as characters [6], digits, faces [2], and topographic
maps [3]. Curve fitting [18][19] is the process of constructing a 2nd order or higher mathematical
function that best fits to a series of landmark points. A related topic is a regression analysis which
stresses on probabilistic conclusion on how uncertainty occurs when fitting a curve to a set of data
landmarks with marginal errors. Regression curves are applied in data visualization [12][13] to
capture the values of a function with missing data [14] and to gain relationship between multiple
variables.
In this paper, we demonstrate how curves are used to recognize 2D handwritten shapes by applying
2nd order of polynomial quadratic function to a set of landmark points presented in a shape. We
then train such curves to capture the optimal characteristics of multiple shapes in the training set.
Handwritten Arabic characters are used and tested in this investigation.
2.REGRESSION CURVES
We would like to extract the best fit modes that describe the shapes under study, hence, multiple
image shapes are required and explained through training sets of class shape ω and complete sets
of shape classes denoted by Ω. Let us assume that each training set is represented by the following
2D training patterns as a long vector
= ( , , . . , , , , , . . , , , , , . . , , ) (1)
Our model here is a polynomial of a higher order.In this example, we choose 2nd
order of quadratic
curves. Consider the following generic form for polynomial of order j
= + + ( ) + ( ) + ⋯ + ( )
= + ( )
(2)
The nonlinear regression above requires the estimation of the coefficients thatbest fit the sample
shape land marks, we approach the least square error between the data y and f(x) in
= ( !) = ( − ( )) + ( − ( )) + ( − ( )) + ( #
− ( # ))
(3)
3. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.10, No.3, May 2019
51
where the goal is to minimize the error, we substitute the form of equation (3) with a general least
square error
= (
$
− ( + + + + ⋯ + )) (4)
where T is the number of pattern set, k is the current data landmark point being summed, jis the
order of polynomial equation. Rewriting equation (4) in a more readable format
= ( − ( + ))
$
(5)
Finding the best fitting curve is equivalent to minimize the squared distance between the curve and
landmark points. The aim here is to find the coefficients, hence, solving the equations by taking the
partial derivative with respect each coefficient a0, .. ,ak; for k = 1 .. j and set each to zero in
%
%
= ( − (
$
+ )) = 0
$
'
(6)
%
%
= ( − (
$
+ )) = 0
$
'
(7)
%
%
= ( − (
$
+ )) = 0
$
'
(8)
Rewriting upper equations in the form of a matrix and applying linear algebra matrix differentiation
, we get
(
)
)
)
)
)
)
)
* +
$ $
$ $ $
$ $
#
$
,
-
-
-
-
-
-
-
.
/ 0 =
(
)
)
)
)
)
)
)
*
$
$
$
,
-
-
-
-
-
-
-
.
(9)
Choosing Gaussian elimination procedure to rewrite the upper equation in more solvable in
1 = 2 (10)
where
4. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.10, No.3, May 2019
52
A =
(
)
)
* + ∑$ ∑$
∑$
∑$
∑$
∑$ ∑$ ∑
#$
,
-
-
.
,
X = / 0,
B = 4
∑$
∑$
∑$
5
(11)
solving X to find the coefficients A, B in
= 16
∗ 2 (12)
The outcome would be the coefficients a0, a1, a2. We follow the similar procedure to find the
coefficient sets of the remaining landmark points.With these coefficients models at hand, it is
possible to project them in order to generate a sample shape similar to those in the training patterns.
The extracted coefficients are then plugged in to a set of corresponding coordinate points which
results in a new shape in
8 = (9 , = ∗ : + ∗ : + : ;
<, 9 ,
= ∗ : + ∗ : + : ;
< , .. , 9 , = ( ) ∗ :=
+ ∗ :=
+ :=;
<)
(13)
3.LEARNING REGRESSION CURVES
It has been acknowledge that when using learning algorithms to train models of suchcase, the
outcome is trained models with superior performance to those of untrained models Bishop [19]. In
this stage, we are concerned with capturing the optimal curve coefficients which describe the
patterns variations under testing; hence, training is required , thereby, fitting the Gaussian mixtures
to curve coefficient models to a set of shape curve patterns. The previous approaches consider
producing variations in shapes in a linear fashion. To obtain more complex shape variations, we
have to proceed by employing non-linear deformation to a set of curve coefficients. Unsupervised
learning is encapsulated in a framework of the apparatus Expectation Maximization EM
Algorithm.The idea is borrowed from Cootes[20] who was the pioneer in constructing point
distribution models; however, the algorithm introduced by Cootes[20] is transformed to learn
regression curves coefficientsαtsimilar to that approach of AlShaher [21]. Suppose that a set of
curve coefficients αt for a set of training patterns is t = (1 … T ) where T is the complete set of
training curves is represented in a long vector of coefficients :
>: = ( : , : , : ;
, : , : , : ;
, . . . , :?@ :?@
) (14)
The mean vector of coefficient patterns is represented by
A =
1
+
>:
$
:
(15)
5. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.10, No.3, May 2019
53
The covariance matrix is then constructed by
=
1
+
(>: − A
$
:
) (>: − A)$ (16)
The following approach is based on fitting a Gaussian mixture modelsto a set of training examples
of curve coefficients. We further assume that training patterns are independent from one to another;
thus, they are neither flagged nor labelled to any curve class. Each curve class ω belongs to a set of
curve classes Ω has its own mean µ and covariance matrix ∑. With these component elements. For
each curve class, we establish the likelihood function for a set of the curve patterns in
C(>:) = D C(>:
E
F
|AF
$
:
, ∑F) (17)
Where the term C(>:|AF, ∑F) is the probability of drawing curve pattern αtfrom the curve-class
ω.Associating the above likelihood function with the Expectation Maximization Algorithm, the
likelihood function can be a process reformed in two steps.The process revolves around estimating
the expected log-likelihood function iteratively in
HI J(KL )
MJ(K)
= N(>:, AF
(K)
, ∑F
(K)
)
E
F
ln C(>:|AF
(KL )
, ∑F
(KL )
$
:
) (18)
Where the quantity and AF
(K)
and ∑F
(K)
are the estimated mean curve vector and variance
covariance matrix both at iteration (n) of the Algorithm. The quantity C(>:, AF
(K)
, ∑ F
(K)
) is the a
posteriori probability that the training pattern curve belongs to the curve-class ω at iteration n of the
Algorithm. The termC(>:|AF
(KL )
, ∑ F
(KL )
) is the probability of distribution of curve-pattern αt
belongs to curve-class ω at iteration ( n + 1 )of the algorithm; thus, the probability density is
associated with the curve- patterns αtfor ( t = 1 … T ) to curve-class ω are estimated by the updated
construction of the mean-vector AF
(KL )
, and covariance matrix ∑F
(KL )
at iteration n+1 of the
algorithm. According to the EM algorithm, the expected log-likelihood function is implemented in
a two iterative processes. In the M or maximization step of the algorithm, our aim is to maximize
the curve mean-vector AF
(KL )
, and covariance matrix ∑F
(KL )
, while, in the E or expectation step,
the aim is to estimate the distribution of curve-patterns at iteration n along with the mixing
proportion parameters for curve-class ω.
In the E, or Expectation step of the algorithm, the a posteriori curve-class probability is updated
by applying the Bayes factorization rule to the curve-class distribution density at iteration n+1. The
new estimate is computed by
C Q∝:, AF
(K)
,
F
(K)
S =
C(>:|AF
(K)
, ∑F
(K )
) TF
(K)
∑F
E
C(>:|AF
(K)
, ∑F
(K )
) TF
(K)
(19)
where the revised curve-class ω mixing proportions TF
(KL )
at iteration ( n + 1 )is computed by
6. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.10, No.3, May 2019
54
TF
(KL )
=
1
+
C(>:|AF
(K)
, ∑F
(K )
)
$
:
(20)
With that at hand, the distributed curve-pattern αt to the class-curve ω is Gaussian distribution and
is classified according to
C Q>:UAF
(K)
,
F
(K)
S =
1
(2T)IW| ∑ F
(K)
|
exp /−
1
2
>: − AF
(K) $
Q
F
(K)
S
6
(>: − AF
(K)
)0 (21)
In the M, or Maximization step, our goal is to maximize the curve-class ω parameters. The updated
curve mean-vector AF
(KL )
estimate is computed using the following
AF
(KL )
= C(>:, AF
(K)
, ∑F
(K)
)>:
$
:
(22)
And the new estimate of the curve-class covariance matrix is weighted by
∑F
(KL )
= C Q>:, AF
(K)
,
F
(K)
S
$
:
(>: − AF
(K)
) >: − AF
(K) $
(23)
Both E, and M steps are iteratively converged, the outcome of the learning stage is a set of
curve-class ω parameters such as AF
(K)
[ ∑F
(K )
, hence the complete set of all curve-class Ω are
computed and ready to be used for recognition.
With the stroke and shape point distribution models to hand, our recognition method proceeds in a
hierarchical manner.
4.RECOGNITION
In this stage, we focus on utilizing the parameters extracted from the learning phase to obtain in
shape recognition. Here, we assume that the testing shapes
() = ( :?
K
!
]
:
, :?
), ^ℎ (` = 1 . . [), ( = 1 . . ) (24)
Hence, each testing pattern is represented by
a: = ( : , : , : , : , … :?
, :?
) for (t = 1 .. X ) (25)
Such testing patterns are classified according to computing the new point position of the testing
data χ after projecting the sequence of curve-coefficients by
7. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.10, No.3, May 2019
55
( , ) = (a:c?
− ( >:?
a:d?
+
K
!
>:?
a:d?
+ >:?;
)) (26)
So the sample shape χt is registered to class ω which has the highest probability using Bayes rule
over the total curve-classes Ω in
arg min
( , )
∑ ( , )E
F
(27)
5.EXPERIMENTS
We have evaluated our approach with sets of Arabic handwritten characters. Here, we have used 23
shape-classes for different writers, each with 80 training patterns. In total, we have examined the
approach with 1840 handwritten Arabic character shape patterns for training and 4600 patterns for
recognition phase. Figures 1illustrates some training patterns used in this paper. Figure 2
demonstrates single shapes and their landmarks representation.
Figure 1: Training sets sample
Figure 2: Training patterns and extracted landmarks
Figure 3: Sample visual of regression Curve-classes
Figure 3indicates regression sample curve-classes as a result of the training stage. Figure 4
demonstrates the curve-classes Ω convergence rate graph as a function per iteration no. in the
training phase. The graphs shows how associated distributed probabilities for the set of
curve-classes Ω converged into a few iterations. Table 1 shows sample curve coefficients for
different shapes under investigation.
8. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.10, No.3, May 2019
56
To take this investigation further, we demonstrate how well the approach behaves in the presence
of noise. In figure 5, we show how recognition rate is achieved when point position displacement
error is applied. Test shape coordinates are being moved away from their original position. The
figure proves that the recognition rate fails to register shapes to their correct classes in a few
iterations and it decreases completely when coordinates are moved away, yet,increasing variance
significantly.
Figure 4: Convergence Rate as a function per
iteration no.
Figure 5: Recognition rate as a function per iteration
no with point position error.
Table 1: Sample Curve Coefficients for chosen shapes
Coefficient
s
Shape
: : : ; : : : ; := := :=;
Lam -0.01452 3.222 -168.4 -0.006319 1.336 -42.01 -0.09073 20.1 -1053
Ain 0.1667 -23.5 832.3 -0.7917 102.1 -3269 -0.645 60.84 -1415
Baa -0.091 26.74 -1905 0.3 -86 6390 0.2911 -86 6390
Haa 0.334 -22.17 387.1 0.31 -25.3 539 0.1333 -14.87 436.8
Kaf -0.1667 40 -2366 0.25 -53 2844 0.3665 -69.97 3374
Table 2 shows recognition rates per curve-classes ω. Table 1 demonstrates recognition rates per
curve-class. In total, we have achieved 94% recognition rate with this approach.
Table 2:
Recogniti
on Rates
for sample
shapesSa
mple
Shape
Tes
t Size
Cor
rect
Fal
se
Recog
nition
Rate
Sample
Shape
Test
Size
Corr
ect
Fal
se
Reco
gnition
Rate
200 191 9 95.5% 200 176 24 88%
200 193 7 96.5% 200 175 25
87.5
%
200 183 17 91.5% 200 172 28 86%
9. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.10, No.3, May 2019
57
200 187 13 93.5% 200 181 19
90.5
%
200 196 4 98% 200 190 10 95%
200 180 20 90% 200 182 18 91%
200 178 22 89% 200 193 7
96.5
%
6.CONCLUSION
In this paper, we have proved how Regression Curves can be utilized to model the variation of
Handwritten Arabic characters. A 2nd order of Polynomials curves are injected along the skeleton
of the proposed shape under study, where the appropriate set of curve-coefficients which describe
the shape were extracted. We, then have used the Apparatus of the Expectation Maximization
Algorithm to train the set of extracted set of curve-coefficients within a probabilistic framework to
capture the optimal shape variations coefficients. The set of best fitted parameters are then
projected to recognize handwritten shapes using Bayes rule of factorization. The proposed
approach has been evaluated on sets of Handwritten Arabic Shapes for multiple different writers by
which we have achieved a recognition rate of nearly 94% on corrected registered shape classes.
7. ACKNOWLEDGMENT
I would like to express my sincere gratitude to the Public Authority for Applied Education and
Training ( Research Department ) for their full funding to this research. Research project number
BS-17-06 under the title "Arabic Character Recognition". Their kind support including hardware,
software, books, and conference fees allowed me to investigate and conduct this research with
significant results. Their attention permitted me to contribute a good result to the literature of
Pattern Recognition field specifically in recognizing handwritten Arabic characters.
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