This document discusses support vector machines (SVMs) and their application in agriculture. It begins with an introduction to SVMs, explaining that they are a supervised machine learning algorithm used for classification and regression. The document then covers key aspects of SVMs including: how they find the optimal separating hyperplane for classification; handling linearly separable and non-separable data using soft-margin hyperplanes and kernels; and common kernel functions. It provides an example application of using an SVM classifier to identify pests in leaf images. In conclusion, the document provides an overview of SVMs and their use in solving agricultural classification problems.
1. The document discusses various machine learning classification algorithms including neural networks, support vector machines, logistic regression, and radial basis function networks.
2. It provides examples of using straight lines and complex boundaries to classify data with neural networks. Maximum margin hyperplanes are used for support vector machine classification.
3. Logistic regression is described as useful for binary classification problems by using a sigmoid function and cross entropy loss. Radial basis function networks can perform nonlinear classification with a kernel trick.
MARGINAL PERCEPTRON FOR NON-LINEAR AND MULTI CLASS CLASSIFICATION ijscai
Generalization error of classifier can be reduced by larger margin of separating hyperplane. The proposed classification algorithm implements margin in classical perceptron algorithm, to reduce generalized errors by maximizing margin of separating hyperplane. Algorithm uses the same updation rule with the perceptron, to converge in a finite number of updates to solutions, possessing any desirable fraction of the margin. This solution is again optimized to get maximum possible margin. The algorithm can process linear, non-linear and multi class problems. Experimental results place the proposed classifier equivalent to the support vector machine and even better in some cases. Some preliminary experimental results are briefly discussed.
Anomaly detection using deep one class classifier홍배 김
The document discusses anomaly detection techniques using deep one-class classifiers and generative adversarial networks (GANs). It proposes using an autoencoder to extract features from normal images, training a GAN on those features to model the distribution, and using a one-class support vector machine (SVM) to determine if new images are within the normal distribution. The method detects and localizes anomalies by generating a binary mask for abnormal regions. It also discusses Gaussian mixture models and the expectation-maximization algorithm for modeling multiple distributions in data.
For more info visit us at: http://www.siliconmentor.com/
Support vector machines are widely used binary classifiers known for its ability to handle high dimensional data that classifies data by separating classes with a hyper-plane that maximizes the margin between them. The data points that are closest to hyper-plane are known as support vectors. Thus the selected decision boundary will be the one that minimizes the generalization error (by maximizing the margin between classes).
A Fuzzy Interactive BI-objective Model for SVM to Identify the Best Compromis...ijfls
This document summarizes a research paper that proposes a fuzzy bi-objective support vector machine (SVM) model to identify infected COVID-19 patients. The model uses SVM classification with two objectives - maximizing margin between classes and minimizing misclassification errors. An α-cut transforms the fuzzy model into a classical bi-objective problem solved using weighting methods. This generates multiple efficient solutions. An interactive process then identifies the best compromise based on minimizing the number of support vectors in each class. The model constructs a utility function to measure COVID-19 infection levels based on the SVM classification.
A FUZZY INTERACTIVE BI-OBJECTIVE MODEL FOR SVM TO IDENTIFY THE BEST COMPROMIS...ijfls
A support vector machine (SVM) learns the decision surface from two different classes of the input points. In several applications, some of the input points are misclassified and each is not fully allocated to either of these two groups. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. For the proposed fuzzy bi-objective quadratic programming model, a major contribution will be added by obtaining different effective support vectors due to changes in weighting values. The experimental results, show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions. The main contribution of this paper includes constructing a utility function for measuring the degree of infection with coronavirus disease (COVID-19).
This document provides an overview of kernel machines and the kernel trick in machine learning. It discusses how the kernel trick allows projecting data into a higher dimensional space to make it linearly separable. It describes using kernels like polynomial kernels in the dual formulation to calculate dot products without explicitly performing the projection. The kernel trick avoids having to compute in the higher dimensional space, improving computational efficiency.
1. The document discusses various machine learning classification algorithms including neural networks, support vector machines, logistic regression, and radial basis function networks.
2. It provides examples of using straight lines and complex boundaries to classify data with neural networks. Maximum margin hyperplanes are used for support vector machine classification.
3. Logistic regression is described as useful for binary classification problems by using a sigmoid function and cross entropy loss. Radial basis function networks can perform nonlinear classification with a kernel trick.
MARGINAL PERCEPTRON FOR NON-LINEAR AND MULTI CLASS CLASSIFICATION ijscai
Generalization error of classifier can be reduced by larger margin of separating hyperplane. The proposed classification algorithm implements margin in classical perceptron algorithm, to reduce generalized errors by maximizing margin of separating hyperplane. Algorithm uses the same updation rule with the perceptron, to converge in a finite number of updates to solutions, possessing any desirable fraction of the margin. This solution is again optimized to get maximum possible margin. The algorithm can process linear, non-linear and multi class problems. Experimental results place the proposed classifier equivalent to the support vector machine and even better in some cases. Some preliminary experimental results are briefly discussed.
Anomaly detection using deep one class classifier홍배 김
The document discusses anomaly detection techniques using deep one-class classifiers and generative adversarial networks (GANs). It proposes using an autoencoder to extract features from normal images, training a GAN on those features to model the distribution, and using a one-class support vector machine (SVM) to determine if new images are within the normal distribution. The method detects and localizes anomalies by generating a binary mask for abnormal regions. It also discusses Gaussian mixture models and the expectation-maximization algorithm for modeling multiple distributions in data.
For more info visit us at: http://www.siliconmentor.com/
Support vector machines are widely used binary classifiers known for its ability to handle high dimensional data that classifies data by separating classes with a hyper-plane that maximizes the margin between them. The data points that are closest to hyper-plane are known as support vectors. Thus the selected decision boundary will be the one that minimizes the generalization error (by maximizing the margin between classes).
A Fuzzy Interactive BI-objective Model for SVM to Identify the Best Compromis...ijfls
This document summarizes a research paper that proposes a fuzzy bi-objective support vector machine (SVM) model to identify infected COVID-19 patients. The model uses SVM classification with two objectives - maximizing margin between classes and minimizing misclassification errors. An α-cut transforms the fuzzy model into a classical bi-objective problem solved using weighting methods. This generates multiple efficient solutions. An interactive process then identifies the best compromise based on minimizing the number of support vectors in each class. The model constructs a utility function to measure COVID-19 infection levels based on the SVM classification.
A FUZZY INTERACTIVE BI-OBJECTIVE MODEL FOR SVM TO IDENTIFY THE BEST COMPROMIS...ijfls
A support vector machine (SVM) learns the decision surface from two different classes of the input points. In several applications, some of the input points are misclassified and each is not fully allocated to either of these two groups. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. For the proposed fuzzy bi-objective quadratic programming model, a major contribution will be added by obtaining different effective support vectors due to changes in weighting values. The experimental results, show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions. The main contribution of this paper includes constructing a utility function for measuring the degree of infection with coronavirus disease (COVID-19).
This document provides an overview of kernel machines and the kernel trick in machine learning. It discusses how the kernel trick allows projecting data into a higher dimensional space to make it linearly separable. It describes using kernels like polynomial kernels in the dual formulation to calculate dot products without explicitly performing the projection. The kernel trick avoids having to compute in the higher dimensional space, improving computational efficiency.
Support vector machines (SVMs) are a supervised machine learning algorithm used for classification and regression analysis. SVMs find the optimal boundary, known as a hyperplane, that separates classes of data. This hyperplane maximizes the margin between the two classes. Extensions to the basic SVM model include soft margin classification to allow some misclassified points, methods for multi-class classification like one-vs-one and one-vs-all, and the use of kernel functions to handle non-linear decision boundaries. Real-world applications of SVMs include face detection, text categorization, image classification, and bioinformatics.
This document provides an overview of support vector machines and kernel methods for machine learning.
It discusses how preprocessing input data with nonlinear features can make classification problems linearly separable in high-dimensional space. However, directly using all possible features risks overfitting.
Support vector machines find a maximum-margin separating hyperplane in feature space to minimize overfitting. They use only a subset of training points, called support vectors, to define the decision boundary.
The kernel trick allows support vector machines to implicitly operate in very high-dimensional feature spaces without explicitly computing the feature vectors. All computations can be done using kernel functions that evaluate scalar products in feature space. This makes support vector machines computationally feasible even for huge feature spaces
Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression analysis. It finds a hyperplane in an N-dimensional space that distinctly classifies data points. SVM is effective in high-dimensional spaces and with limited training data, and can perform nonlinear classification using kernel tricks. The objective is to find the hyperplane that has the largest distance to the nearest training data points of any class, since these are the hardest to classify correctly.
Data Science - Part IX - Support Vector MachineDerek Kane
This lecture provides an overview of Support Vector Machines in a more relatable and accessible manner. We will go through some methods of calibration and diagnostics of SVM and then apply the technique to accurately detect breast cancer within a dataset.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
1) The document discusses sparse kernel machines and support vector machines (SVMs). It covers the optimization of SVMs using Lagrange multipliers and the Karush-Kuhn-Tucker (KKT) conditions.
2) SVMs find the maximum-margin separating hyperplane between two classes of data points by maximizing the margin between the closest data points of each class. This is done by solving the dual optimization problem.
3) Support vectors are the data points that lie on the boundary or margin of the classifier. Only the support vectors are needed for making predictions on new data using the SVM model.
Analytical study of feature extraction techniques in opinion miningcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for
dense growth of researches in the field. One of the important activities of opinion mining is to
extract opinions of people based on characteristics of the object under study. Feature extraction
in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first
part discusses various techniques and second part makes a detailed appraisal of the major
techniques used for feature extraction
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for dense growth of researches in the field. One of the important activities of opinion mining is to extract opinions of people based on characteristics of the object under study. Feature extraction in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first part discusses various techniques and second part makes a detailed appraisal of the major techniques used for feature extraction
Radial Basis Function Neural Network (RBFNN), Induction Motor, Vector control...cscpconf
Although opinion mining is in a nascent stage of development but still the ground is set for dense growth of researches in the field. One of the important activities of opinion mining is to
extract opinions of people based on characteristics of the object under study. Feature extraction in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first part discusses various techniques and second part makes a detailed appraisal of the major techniques used for feature extraction.
Evaluation of a hybrid method for constructing multiple SVM kernelsinfopapers
Dana Simian, Florin Stoica, Evaluation of a hybrid method for constructing multiple SVM kernels, Recent Advances in Computers, Proceedings of the 13th WSEAS International Conference on Computers, Recent Advances in Computer Engineering Series, WSEAS Press, Rodos, Greece, July 23-25, 2009, ISSN: 1790-5109, ISBN: 978-960-474-099-4, pp. 619-623
An evolutionary method for constructing complex SVM kernelsinfopapers
D. Simian, F. Stoica, An Evolutionary Method for Constructing Complex SVM Kernels, Recent Advances in Mathematics and Computers in Biology and Chemistry, Proceedings of the 10th International Conference on Mathematics and Computers in Biology and Chemistry, MCBC’09, Prague, Chech Republic, WSEAS Press, ISBN 978-960-474-062-8, ISSN 1790-5125, pp.172-178, 2009
Single to multiple kernel learning with four popular svm kernels (survey)eSAT Journals
Abstract Machine learning applications and pattern recognition have gained great attention recently because of the variety of applications
depend on machine learning techniques, these techniques could make many processes easier and also reduce the amount of
human interference (more automation). This paper research four of the most popular kernels used with Support Vector Machines
(SVM) for Classification purposes. This survey uses Linear, Polynomial, Gaussian and Sigmoid kernels, each in a single form and
all together as un-weighted sum of kernels as form of Multi-Kernel Learning (MKL), with eleven datasets, these data sets are
benchmark datasets with different types of features and different number of classes, so some will be used with Two-Classes
Classification (Binary Classification) and some with Multi-Class Classification. Shogun machine learning Toolbox is used with
Python programming language to perform the classification and also to handle the pre-classification operations like Feature
Scaling (Normalization).The Cross Validation technique is used to find the best performance Out of the suggested different
kernels' methods .To compare the final results two performance measuring techniques are used; classification accuracy and Area
Under Receiver Operating Characteristic (ROC). General basics of SVM and used Kernels with classification parameters are
given through the first part of the paper, then experimental details are explained in steps and after those steps, experimental
results from the steps are given with final histograms represent the differences of the outputs' accuracies and the areas under
ROC curves (AUC). Finally best methods obtained are applied on remote sensing data sets and the results are compared to a
state of art work published in the field using the same set of data.
Keywords: Machine Learning, Classification, SVM, MKL, Cross Validation and ROC
The document discusses classification and prediction tasks in machine learning. It describes a classification task where a model is trained on expression profiles of leukemia patients and healthy individuals to distinguish between the two. The trained model can then be used to determine if a new patient has leukemia based on their expression profile. It notes challenges like high-dimensional data and limited data. It describes support vector machines and how they can find an optimal separating hyperplane between two classes of data, even when the data is not linearly separable by mapping it to a higher-dimensional space.
- Support vector machines (SVMs) find a linear separator between classes that maximizes the margin between the separator and the nearest data points of each class. This maximum-margin separator generalizes better than other possible separators.
- SVMs can learn nonlinear decision boundaries by mapping data into a high-dimensional feature space and finding a linear separator in that space, which corresponds to a nonlinear separator in the original input space.
- The "kernel trick" allows SVMs to efficiently compute scalar products between points in the high-dimensional feature space without explicitly performing the mapping, making SVMs practical even with huge numbers of features.
- Support vector machines (SVMs) are a machine learning method for classification and regression. They find the optimal separating hyperplane between classes that maximizes the margin between the plane and the closest data points.
- SVMs use a "kernel trick" to efficiently perform computations in high-dimensional feature spaces without explicitly computing the coordinates of data in that space. Common kernels include polynomial and Gaussian radial basis function kernels.
- To classify new examples, SVMs use a decision function that depends on a subset of training samples called support vectors. The model is defined by these support vectors and weights learned during training.
- Support vector machines (SVMs) find a linear separator between classes that maximizes the margin between the separator and the closest data points. This maximum margin separator generalizes better than other separators.
- SVMs can handle non-linear separations by projecting data into a higher-dimensional feature space and finding a linear separator there. The kernel trick allows efficient computation without explicitly using the high-dimensional feature space.
- SVMs solve a convex optimization problem to find the maximum margin separator. Only a subset of data points called support vectors are used to define the separator and classify new data.
This document discusses kernel-based machine learning methods. It covers several topics:
1) Different types of optimization problems for kernel methods, such as linear programming and non-linear programming.
2) Tasks beyond classification/regression like novelty detection, and different types of learning like passive and active learning.
3) Details on training support vector machines and other kernel machines, including decomposition methods and model selection.
4) Different types of kernels including string kernels, and combining kernels for data fusion.
This document provides an overview of support vector machines (SVMs) for machine learning. It explains that SVMs find the optimal separating hyperplane that maximizes the margin between examples of separate classes. This is achieved by formulating SVM training as a convex optimization problem that can be solved efficiently. The document discusses how SVMs can handle non-linear decision boundaries using the "kernel trick" to implicitly map examples to higher-dimensional feature spaces without explicitly performing the mapping.
This chapter discusses classification methods including linear discriminant functions and probabilistic generative and discriminative models. It covers linear decision boundaries, perceptrons, Fisher's linear discriminant, logistic regression, and the use of sigmoid and softmax activation functions. The key points are:
1) Classification involves dividing the input space into decision regions using linear or nonlinear boundaries.
2) Perceptrons and Fisher's linear discriminant find linear decision boundaries by updating weights to minimize misclassification.
3) Generative models like naive Bayes estimate joint probabilities while discriminative models like logistic regression directly model posterior probabilities.
4) Sigmoid and softmax functions are used to transform linear outputs into probabilities for binary and multiclass classification respectively.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Support vector machines (SVMs) are a supervised machine learning algorithm used for classification and regression analysis. SVMs find the optimal boundary, known as a hyperplane, that separates classes of data. This hyperplane maximizes the margin between the two classes. Extensions to the basic SVM model include soft margin classification to allow some misclassified points, methods for multi-class classification like one-vs-one and one-vs-all, and the use of kernel functions to handle non-linear decision boundaries. Real-world applications of SVMs include face detection, text categorization, image classification, and bioinformatics.
This document provides an overview of support vector machines and kernel methods for machine learning.
It discusses how preprocessing input data with nonlinear features can make classification problems linearly separable in high-dimensional space. However, directly using all possible features risks overfitting.
Support vector machines find a maximum-margin separating hyperplane in feature space to minimize overfitting. They use only a subset of training points, called support vectors, to define the decision boundary.
The kernel trick allows support vector machines to implicitly operate in very high-dimensional feature spaces without explicitly computing the feature vectors. All computations can be done using kernel functions that evaluate scalar products in feature space. This makes support vector machines computationally feasible even for huge feature spaces
Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression analysis. It finds a hyperplane in an N-dimensional space that distinctly classifies data points. SVM is effective in high-dimensional spaces and with limited training data, and can perform nonlinear classification using kernel tricks. The objective is to find the hyperplane that has the largest distance to the nearest training data points of any class, since these are the hardest to classify correctly.
Data Science - Part IX - Support Vector MachineDerek Kane
This lecture provides an overview of Support Vector Machines in a more relatable and accessible manner. We will go through some methods of calibration and diagnostics of SVM and then apply the technique to accurately detect breast cancer within a dataset.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
1) The document discusses sparse kernel machines and support vector machines (SVMs). It covers the optimization of SVMs using Lagrange multipliers and the Karush-Kuhn-Tucker (KKT) conditions.
2) SVMs find the maximum-margin separating hyperplane between two classes of data points by maximizing the margin between the closest data points of each class. This is done by solving the dual optimization problem.
3) Support vectors are the data points that lie on the boundary or margin of the classifier. Only the support vectors are needed for making predictions on new data using the SVM model.
Analytical study of feature extraction techniques in opinion miningcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for
dense growth of researches in the field. One of the important activities of opinion mining is to
extract opinions of people based on characteristics of the object under study. Feature extraction
in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first
part discusses various techniques and second part makes a detailed appraisal of the major
techniques used for feature extraction
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for dense growth of researches in the field. One of the important activities of opinion mining is to extract opinions of people based on characteristics of the object under study. Feature extraction in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first part discusses various techniques and second part makes a detailed appraisal of the major techniques used for feature extraction
Radial Basis Function Neural Network (RBFNN), Induction Motor, Vector control...cscpconf
Although opinion mining is in a nascent stage of development but still the ground is set for dense growth of researches in the field. One of the important activities of opinion mining is to
extract opinions of people based on characteristics of the object under study. Feature extraction in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first part discusses various techniques and second part makes a detailed appraisal of the major techniques used for feature extraction.
Evaluation of a hybrid method for constructing multiple SVM kernelsinfopapers
Dana Simian, Florin Stoica, Evaluation of a hybrid method for constructing multiple SVM kernels, Recent Advances in Computers, Proceedings of the 13th WSEAS International Conference on Computers, Recent Advances in Computer Engineering Series, WSEAS Press, Rodos, Greece, July 23-25, 2009, ISSN: 1790-5109, ISBN: 978-960-474-099-4, pp. 619-623
An evolutionary method for constructing complex SVM kernelsinfopapers
D. Simian, F. Stoica, An Evolutionary Method for Constructing Complex SVM Kernels, Recent Advances in Mathematics and Computers in Biology and Chemistry, Proceedings of the 10th International Conference on Mathematics and Computers in Biology and Chemistry, MCBC’09, Prague, Chech Republic, WSEAS Press, ISBN 978-960-474-062-8, ISSN 1790-5125, pp.172-178, 2009
Single to multiple kernel learning with four popular svm kernels (survey)eSAT Journals
Abstract Machine learning applications and pattern recognition have gained great attention recently because of the variety of applications
depend on machine learning techniques, these techniques could make many processes easier and also reduce the amount of
human interference (more automation). This paper research four of the most popular kernels used with Support Vector Machines
(SVM) for Classification purposes. This survey uses Linear, Polynomial, Gaussian and Sigmoid kernels, each in a single form and
all together as un-weighted sum of kernels as form of Multi-Kernel Learning (MKL), with eleven datasets, these data sets are
benchmark datasets with different types of features and different number of classes, so some will be used with Two-Classes
Classification (Binary Classification) and some with Multi-Class Classification. Shogun machine learning Toolbox is used with
Python programming language to perform the classification and also to handle the pre-classification operations like Feature
Scaling (Normalization).The Cross Validation technique is used to find the best performance Out of the suggested different
kernels' methods .To compare the final results two performance measuring techniques are used; classification accuracy and Area
Under Receiver Operating Characteristic (ROC). General basics of SVM and used Kernels with classification parameters are
given through the first part of the paper, then experimental details are explained in steps and after those steps, experimental
results from the steps are given with final histograms represent the differences of the outputs' accuracies and the areas under
ROC curves (AUC). Finally best methods obtained are applied on remote sensing data sets and the results are compared to a
state of art work published in the field using the same set of data.
Keywords: Machine Learning, Classification, SVM, MKL, Cross Validation and ROC
The document discusses classification and prediction tasks in machine learning. It describes a classification task where a model is trained on expression profiles of leukemia patients and healthy individuals to distinguish between the two. The trained model can then be used to determine if a new patient has leukemia based on their expression profile. It notes challenges like high-dimensional data and limited data. It describes support vector machines and how they can find an optimal separating hyperplane between two classes of data, even when the data is not linearly separable by mapping it to a higher-dimensional space.
- Support vector machines (SVMs) find a linear separator between classes that maximizes the margin between the separator and the nearest data points of each class. This maximum-margin separator generalizes better than other possible separators.
- SVMs can learn nonlinear decision boundaries by mapping data into a high-dimensional feature space and finding a linear separator in that space, which corresponds to a nonlinear separator in the original input space.
- The "kernel trick" allows SVMs to efficiently compute scalar products between points in the high-dimensional feature space without explicitly performing the mapping, making SVMs practical even with huge numbers of features.
- Support vector machines (SVMs) are a machine learning method for classification and regression. They find the optimal separating hyperplane between classes that maximizes the margin between the plane and the closest data points.
- SVMs use a "kernel trick" to efficiently perform computations in high-dimensional feature spaces without explicitly computing the coordinates of data in that space. Common kernels include polynomial and Gaussian radial basis function kernels.
- To classify new examples, SVMs use a decision function that depends on a subset of training samples called support vectors. The model is defined by these support vectors and weights learned during training.
- Support vector machines (SVMs) find a linear separator between classes that maximizes the margin between the separator and the closest data points. This maximum margin separator generalizes better than other separators.
- SVMs can handle non-linear separations by projecting data into a higher-dimensional feature space and finding a linear separator there. The kernel trick allows efficient computation without explicitly using the high-dimensional feature space.
- SVMs solve a convex optimization problem to find the maximum margin separator. Only a subset of data points called support vectors are used to define the separator and classify new data.
This document discusses kernel-based machine learning methods. It covers several topics:
1) Different types of optimization problems for kernel methods, such as linear programming and non-linear programming.
2) Tasks beyond classification/regression like novelty detection, and different types of learning like passive and active learning.
3) Details on training support vector machines and other kernel machines, including decomposition methods and model selection.
4) Different types of kernels including string kernels, and combining kernels for data fusion.
This document provides an overview of support vector machines (SVMs) for machine learning. It explains that SVMs find the optimal separating hyperplane that maximizes the margin between examples of separate classes. This is achieved by formulating SVM training as a convex optimization problem that can be solved efficiently. The document discusses how SVMs can handle non-linear decision boundaries using the "kernel trick" to implicitly map examples to higher-dimensional feature spaces without explicitly performing the mapping.
This chapter discusses classification methods including linear discriminant functions and probabilistic generative and discriminative models. It covers linear decision boundaries, perceptrons, Fisher's linear discriminant, logistic regression, and the use of sigmoid and softmax activation functions. The key points are:
1) Classification involves dividing the input space into decision regions using linear or nonlinear boundaries.
2) Perceptrons and Fisher's linear discriminant find linear decision boundaries by updating weights to minimize misclassification.
3) Generative models like naive Bayes estimate joint probabilities while discriminative models like logistic regression directly model posterior probabilities.
4) Sigmoid and softmax functions are used to transform linear outputs into probabilities for binary and multiclass classification respectively.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
2. 2
2
2
Presented by,
HARISH NAYAK, G.H.
PALB 9202
Support Vector Machine And it’s
Application in Agriculture
First seminar
on
AGRICULTURAL
STATISTICS
3. Flow of seminar
1. Introduction
2. SVM
3. Linear and Nonlinear separable case
4. Kernel functions
5. Case study
6. Conclusion
7. References
4. Introduction
o SVM was introduced by
Vladimir Vapnik in 1995 as a
kernel based machine learning
model for classification and
regression task.
o SVM has been used as a
powerful tool for solving
practical binary classification
problems.
4
5. What is SVM ?
SVM is a supervised machine learning
algorithm which is mainly used to classify
data into different classes. Unlike most
algorithms, SVM makes use of a hyperplane
which acts like a decision boundary
between the various classes.
5
6. Features of SVM
1. SVM is a supervised learning
algorithm (i.e., it trains on a set of
labelled data).
o SVM studies the labelled training data
and then classifies any new input data
depending on what it learned in the
training phase.
6
7. Cont...
2. SVM can be used for both
‘classification’ and ‘regression’
problems.
o However, it is mainly used for
classification problems.
7
8. Cont...
3. SVM is used for classifying non-linear
data by using the ‘kernel trick’.
8
9. Principle of SVM
o The formulation of SVM learning is based on the
principle of Structural Risk Minimization[SRM]
(Vapnik, 2000).
o SVM allows to maximize the generalization ability of a
model. This is the objective of the SRM principle that
allows the minimization of a bound on the
generalization error of a model, instead of minimizing
the mean squared error on the set of training data,
which is often used by empirical risk minimization.
9
11. There are two cases in the dataset:
1. Linearly separable case: The data can be
separated into two or more classes
clearly. There will be no overlapping (or)
no intersection.
2. Non-Linearly separable case: The data
can not be separated into two or more
classes clearly. There will be overlapping
(or) presence of intersection.
11
12. Linearly separable case
o Each dataset consists of a pair, an vector 𝑥𝑖
(input vector) and the associated label 𝑦𝑖.
o Let training set 𝑋 be:
𝑥1, 𝑦1 , 𝑥2, 𝑦2 , …, 𝑥𝑛, 𝑦𝑛
i.e., 𝑋 = 𝑥𝑖, 𝑦𝑖 𝑖=1
𝑛
where 𝑥𝑖 ∈ 𝑅𝑑
[d-dimensions]
and 𝑦𝑖∈ +1, −1 .
12
13. CONT...
o For visualization purpose, we will consider 2-
dimensional input, i.e., 𝑥𝑖 ∈ 𝑅2
o The data are linearly separable.
o There are infinite number of hyperplanes that can
perform the separation of the data.
o Fig.1 shows several decision hyperplanes that
perfectly separate the input data set.
13
15. Cont...
o Among all the hyperplanes, the one with
maximum margin and good
generalization ability will be selected.
o This hyperplane is called “Optimal
separation hyperplane”.(Fig.2)
15
17. Cont...
o The hyperplane that separates the input space is
defined by the equation
𝑤𝑇
𝑥𝑖 + 𝑏 = 0 …(1) [𝑖 = 1, … , 𝑁]
o It can be fitted to correctly classify training
patterns, where
a. The weight vector ‘𝑤’ is normal to the hyperplane,
and defines its orientation,
b. ‘𝑏’ is the bias term and
c. ‘T’ is the transpose
17
18. Cont...
From Equation (1), the linear classifier (decision function),
given by
𝑦(𝑥)=𝑠𝑖𝑔𝑛( 𝑤𝑇𝑥+𝑏) …(2)
classifying
Class 2 (𝑦𝑖 = +1 if 𝑤𝑇𝑥 + 𝑏 ≥ 0) and
Class 1(𝑦𝑖 = −1 if 𝑤𝑇
𝑥 + 𝑏 ≤ 0) patterns
18
19. Cont...
𝑤𝑇
𝑥𝑖 + 𝑏 ≥ +1 𝑖𝑓 𝑦𝑖 = +1
𝑤𝑇𝑥𝑖 + 𝑏 ≤ −1 𝑖𝑓 𝑦𝑖 = −1 , 𝑖 = 1,2, … 𝑁
𝑚𝑖𝑛𝑤,𝑏 𝑤. 𝑤 = ||𝑤||2
This can be combined into a single set of equalities:
𝑦𝑖 𝑤𝑇𝑥𝑖 + 𝑏 − 1 ≥ 0, …(∗) 𝑖 = 1,2, … 𝑁
where 𝑁 is the training data size.
o For maximal separation, the hyperplane should be as far away as
possible from each of them.
19
20. Cont...
Solving the equations:
𝑤𝑇
𝑥1 + 𝑏 = +1 and 𝑤𝑇
𝑥2 + 𝑏 = −1
𝑤𝑇
𝑥1 − 𝑥2 = +2
𝑤
||𝑤||
. 𝑥1 − 𝑥2 =
2
||𝑤||
where 𝑤 = 𝑤𝑇𝑤 = 𝑤1
2
+ 𝑤2
2
+ ⋯ + 𝑤𝑛
2
is the norm of a
vector. (Fig.3)
20
21. Cont...
o The distance between the hyperplane and
the training data closest to the
hyperplane is called ‘margin’.
o The geometric margin of 𝑥+
y𝑥−
is
𝛾𝑖 =
1
2
𝑤
||𝑤||
. 𝑥+ −
𝑤
||𝑤||
. 𝑥−
=
1
2||𝑤||
=
1
||𝑤||
21
23. Cont...
o Optimizing the geometric margin means
minimizing the norm of the vector of weights.
o The set of margin-determining training vectors
are called the ‘support vectors’. These are the
data points which are closest to the optimal
hyperplane.
o The solution of an SVM is given only by this
small set of support vectors.
23
24. Cont...
o Construction of hyperplane is same as
the convex Quadratic Programming(QP)
problem.
o The Lagrangian multipliers and Karush-
Kuhn-Tucker (KKT) complimentary
conditions are used to find the optimal
solution.
24
25. o Under condition for optimality, QP problem is finally obtained in
the dual space of Lagrange function.
𝐿 𝑤, 𝑏; 𝛼 =
1
2
||𝑤||2 − 𝑖=1
𝑁
𝛼𝑖 𝑦𝑖 𝑤𝑇𝑥𝑖 + 𝑏 − 1 …(3)
where, Lagrange multipliers: 𝛼𝑖 ≥ 0
o Thus by solving the dual QP problem, the decision function from
Eq.(2) can be rewritten as
𝑓 𝑥 = 𝑠𝑖𝑔𝑛 𝑖=1
𝑁
𝛼𝑖𝑦𝑖𝑥𝑇
𝑥𝑖 + 𝑏 …(4)
o It is the set of positive multiplier that influences the classification,
and their corresponding training vectors are called the support
vectors.
25
26. Limitation of linearly separable case
o The learning problem presented before is valid for
the case where the data is linearly separable, which
means that the training data set has no
intersections.
o However, these problems are rare in the real life.
26
27. NON-SEPARABLE CASE
o We assumed that data is linearly
separable in the previous case. But,
practically it is not always possible.
o The classes in the data sets will have
overlapping and it is not possible to
classify using linear separation
hyperplane.
27
28. Cont...
o Cortes and Vapnik (1993) introduced a modified
maximum margin idea called “Soft margin
hyperplanes”.
o In other words, a linear SVM can be refitted to
learn a hyperplane that is tolerable to a small
number of non-separable training data.
o The approach of refitting is called soft margin
approach, where it introduces slack variables 𝜉𝑖
to the inseparable cases.
28
31. Cont...
o To find a classifier with maximum
margin, the algorithm presented before
should be changed allowing a soft
margin (Fig.4), therefore, it is necessary
to introduce non-negative slack
variables 𝜉𝑖(≥ 0)in the Eq. (*).
𝑦𝑖 𝑤𝑇
𝑥𝑖 + 𝑏 ≥ 1 − 𝜉𝑖 𝑖 = 1,2, … 𝑁
31
32. Cont...
o Due to the slack variables 𝜉𝑖, the feasible solution always
exist.
o If 0 < 𝜉𝑖 < 1, the training data do not have the maximum
margin, but can be correctly classified.
o 𝐶 is the regularization parameter.
o If the value of 𝐶 = ∞ , then there will be no
misclassification.
o However, for non-linear case it is not so. The problem may
be feasible only for some value 𝐶 < ∞.
32
33. o The optimization problem, instead of the conditions of the Eq.(*),
the separation hyperplane should satisfy
𝑚𝑖𝑛𝑤,𝑏,𝜉𝑖
𝑤. 𝑤 + 𝐶
𝑖=1
𝑁
𝜉𝑖
2
such that, 𝑦𝑖 𝑤𝑇𝑥𝑖 + 𝑏 ≥ 1 − 𝜉𝑖, 𝑖 = 1,2, … 𝑁, 𝜉𝑖 ≥ 0
𝑤𝑇
𝑥𝑖 + 𝑏 ≥ +1 − 𝜉𝑖, 𝑦𝑖 = +1, 𝜉𝑖 ≥ 0
𝑤𝑇𝑥𝑖 + 𝑏 ≤ −1 + 𝜉𝑖, 𝑦𝑖 = −1, 𝜉𝑖 ≥ 0
o For the maximum soft margin, the original Lagrangian is
𝐿 𝑤, 𝑏, 𝜉𝑖, 𝛼 =
1
2
𝑤. 𝑤 −
𝑁
𝛼𝑖 𝑦𝑖 𝑤𝑇𝑥𝑖 + 𝑏 − 1 + 𝜉𝑖 +
𝐶
2
𝑁
𝜉𝑖
2
33
34. KERNELS
o In non-linearly separable case, the
classifier may not have a high
generalization ability, even if the
hyperplanes are optimally determined.
o The original input space is transformed
into a highly dimensional space called
“feature space”(refers to n-dimensions).
34
35. What Kernel Trick does?
o A kernel trick is a simple
method where a nonlinear
data is projected onto a higher
dimension space so as to make
it easier to linearly classify the
data by a plane.
35
37. Cont...
o A kernel is a function 𝐾, such that for each 𝑥, 𝑧 ∈ 𝑋
𝐾 𝑥, 𝑧 = 𝜙 𝑥 . 𝜙(𝑧)
Where 𝜙 is a mapping of 𝑋 to a feature space F.
o The decision function is
𝑓 𝑥 =
𝑖=1
𝑁
𝛼𝑖𝑦𝑖𝐾 𝑥𝑖. 𝑥𝑗 + 𝑏
37
38. o A kernel function must satisfy the
following properties, for any 𝑥, 𝑦, 𝑧 ∈ 𝑋
and 𝛼 ∈ 𝑅
1. 𝑥. 𝑥 = 0 𝑜𝑛𝑙𝑦 𝑖𝑓 𝑥 = 0
2. 𝑥. 𝑥 > 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
3. 𝑥. 𝑦 = 𝑦. 𝑥
4. 𝛼𝑥. 𝑦 = 𝛼 𝑥. 𝑦
5. 𝑧 + 𝑥 . 𝑦 = 𝑧. 𝑦 + 𝑥. 𝑦
38
41. Applications of SVM
1. Face detection
2. Text and hypertext categorization
3. Classification of images
4. Bioinformatics
5. Protein fold and remote homology
detection
6. Handwriting recognition
7. Generalized predictive control (GPC)
41
42. Advantages of SVM
1. SVM works relatively well when there is a clear margin of
separation between classes.
2. SVM is more effective in high dimensional spaces.
3. SVM is effective in cases where the number of dimensions is
greater than the number of samples.
4. SVM is relatively memory efficient.
42
44. Architecture:
It consists of various stages such as
image acquisition, preprocessing,
segmentation and accuracy of
infected area. It is calculated by
SVM classifier. The proposed
approach is implemented in
MATLAB.
44
Image Acquisition
Image Preprocessing
Image segmentation
SVM Classifier
Accuracy of infected area
45. a) Image Acquisition
The data set contains pest infected leaf images. The leaves are infected
with Whiteflies.
A sample leaf image with whiteflies
46. b) Image pre-processing
Contrast stretching is an image enhancement technique that improves
the contrast in an Image expanding the dynamic range of intensity
values it contains
46
47. c) Color Based Segmentation Using K-means Clustering
K-Means clustering algorithm is an unsupervised algorithm and it is used to
segment the interested area from the background
There are a lot of applications of the K-mean clustering, range from unsupervised
learning of neural network, Pattern recognitions, Classification analysis, Artificial
intelligent, image processing, machine vision, etc.
47
48. D) SVM Classifier
o A Support vector machine is a powerful tool for binary classification, capable of generating
very fast classifier function following a training period.
Precision=TP/ (TP+FP) :Refers to the percentage of the results which are relevant.
Recall=TP/ (TP+FN) :Refers to the percentage of total relevant results correctly classified by the
algorithm.
Accuracy = (TP+TN)/ (TD+TN+FP+FN) :Number of correct predictions.
48
Positive (+1) Negative(-1)
Positive (+1) True positive False negative
Negative(-1) False positive True negative
50. Conclusion
o Image processing technique plays an important role in the detection
of the pests.
o The pest such as whiteflies, aphids and thrips are very small in size
and infects the leaves.
o The main objective is to detect the pest infected region accuracy in
the leaf image.
o The multiclass svm classifier is used to calculate the accuracy of
infected leaf region.
50
51. SUMMARY
o SVM is a relatively new algorithm proposed for solving
problems in classification.
o SVM can also be used for prediction purpose.
o Kernel trick is the main advantage of SVM, because of
which it has gained more importance.
o SVM can be used in multiple fields of science
depending upon objectives and application domain.
51
52. References
CERVANTES, J., GARCIA-LAMONT, F., RODRÍGUEZ-MAZAHUA,
L. AND LOPEZ, A., 2020, A comprehensive survey on support
vector machine classification: Applications, challenges and
trends, Neurocomputing, 408:189-215.
JAKKULA, V., 2006, Tutorial on support vector machine (svm), School
of EECS, Washington State University, 37.
52
53. Cont...
MOHAN KUMAR, T.L., 2013, Development of Statistical Models using
Nonlinear Support Vector Machines (Doctoral dissertation, IARI-INDIAN
AGRICULTURAL STATISTICS RESEARCH INSTITUTE, NEW DELHI).
RANI, R.U. AND AMSINI, P., 2016, Pest identification in leaf images
using SVM classifier, Int. J. Computational Intelligence and
Informatics, 6(1):248-260.
53
S – represents decision function
h – represents number of data points
Its objective is to minimize both the empirical risk and the confidence interval(capacity of set of functions)
Thus the SRM principle defines a trade-off between the accuracy and complexity of the approximation by minimizing over both terms
Confidence interval or generalization ability
Empirical risk or training error
Weight vector – The weight associated with each input dimension
Weight vector – The weight associated with each input dimension
Bias – The bias is the distance to the origin of the hyperplane solution
We have to Minimize ||w||2 in order to maximize the margin 2/||w||
||w|| represents the magnitude of weight vector
We change this to the dual problem using the Lagrange formulation. There are two reasons to do this.
1. The first lies in the fact that the conditions given will be replaced by Lagrange multipliers, which are much easier to handle.
2. The second is the reformulation of the problem, the training data will only appear in the form of dot product between vectors.
Karush-Kuhn-Tucker conditions (KKT) play a very important role in the theory of optimization, because they give the conditions to obtain an optimal solution to a general optimization problem.
Regularization parameter – how much you want to avoid misclassification.