A tutorial on LDA that first builds on the intuition of the algorithm followed by a numerical example that is solved using MATLAB. This presentation is an audio-slide, which becomes self-explanatory if downloaded and viewed in slideshow mode.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
A tutorial on LDA that first builds on the intuition of the algorithm followed by a numerical example that is solved using MATLAB. This presentation is an audio-slide, which becomes self-explanatory if downloaded and viewed in slideshow mode.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Linear Regression vs Logistic Regression | EdurekaEdureka!
YouTube: https://youtu.be/OCwZyYH14uw
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka PPT on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session:
Types of Machine Learning
Regression Vs Classification
What is Linear Regression?
What is Logistic Regression?
Linear Regression Use Case
Logistic Regression Use Case
Linear Regression Vs Logistic Regression
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
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What is the Expectation Maximization (EM) Algorithm?Kazuki Yoshida
Review of Do and Batzoglou. "What is the expectation maximization algorith?" Nat. Biotechnol. 2008;26:897. Also covers the Data Augmentation and Stan implementation. Resources at https://github.com/kaz-yos/em_da_repo
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Towards Automatic Classification of LOD DatasetsBlerina Spahiu
The datasets that are part of the Linking Open Data cloud
diagram (LOD cloud) are classified into the following topical
categories: media, government, publications, life sciences,
geographic, social networking, user-generated content,
and cross-domain. The topical categories were manually
assigned to the datasets. In this paper, we investigate to
which extent the topical classification of new LOD datasets
can be automated using machine learning techniques and the
existing annotations as supervision. We conducted experiments with different classification techniques and different feature sets. The best classification technique/feature set combination reaches an accuracy of 81.62% on the task of assigning one out of the eight classes to a given LOD dataset. A deeper inspection of the classification errors reveals problems with the manual classification of datasets in the current LOD cloud.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Linear Regression vs Logistic Regression | EdurekaEdureka!
YouTube: https://youtu.be/OCwZyYH14uw
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka PPT on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session:
Types of Machine Learning
Regression Vs Classification
What is Linear Regression?
What is Logistic Regression?
Linear Regression Use Case
Logistic Regression Use Case
Linear Regression Vs Logistic Regression
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
What is the Expectation Maximization (EM) Algorithm?Kazuki Yoshida
Review of Do and Batzoglou. "What is the expectation maximization algorith?" Nat. Biotechnol. 2008;26:897. Also covers the Data Augmentation and Stan implementation. Resources at https://github.com/kaz-yos/em_da_repo
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Towards Automatic Classification of LOD DatasetsBlerina Spahiu
The datasets that are part of the Linking Open Data cloud
diagram (LOD cloud) are classified into the following topical
categories: media, government, publications, life sciences,
geographic, social networking, user-generated content,
and cross-domain. The topical categories were manually
assigned to the datasets. In this paper, we investigate to
which extent the topical classification of new LOD datasets
can be automated using machine learning techniques and the
existing annotations as supervision. We conducted experiments with different classification techniques and different feature sets. The best classification technique/feature set combination reaches an accuracy of 81.62% on the task of assigning one out of the eight classes to a given LOD dataset. A deeper inspection of the classification errors reveals problems with the manual classification of datasets in the current LOD cloud.
Data mining techniques play very important role in health care industry. Liver disease is one of the growing
diseases these days due to the changed life style of people. Various authors have worked in the field of classification of
data and they have used various classification techniques like Decision Tree, Support Vector Machine, Naïve Bayes,
Artificial Neural Network (ANN) etc. These techniques can be very useful in timely and accurate classification and
prediction of diseases and better care of patients. The main focus of this work is to analyze the use of data mining
techniques by different authors for the prediction and classification of liver patient.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
In data streams using classification and clustering different techniques to f...eSAT Journals
Abstract Data stream mining is a process of extracting knowledge from continuous data. Data Stream classification is major challenges than classifying static data because of several unique properties of data streams. Data stream is ordered sequence of instances that arrive at a rate does not store permanently in memory. The problem making more challenging when concept drift occurs when data changes over time Major problems of data stream mining is : infinite length, concept drift, concept evolution. Novel class detection in data stream classification is a interesting research topic for concept drift problem here we compare different techniques for same. Index Terms— Ensemble Method, Decision Tree, Novel Class, Option Tree, Recurring class
MediaEval 2015 - UNED-UV @ Retrieving Diverse Social Images Task - Postermultimediaeval
This paper details the participation of the UNED-UV group at the 2015 Retrieving Diverse Social Images Task. This year, our proposal is based on a multi-modal approach that firstly applies a textual algorithm based on Formal Concept Analysis (FCA) and Hierarchical Agglomerative Clustering (HAC) to detect the latent topics addressed by the images to diversify them according to these topics. Secondly, a Local Logistic Regression model, which uses the low level features and some relevant and non-relevant samples, is adjusted and estimates the relevance probability for all the images in the database.
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
2D Features-based Detector and Descriptor Selection System for Hierarchical R...gerogepatton
Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some
methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for
a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical
classification is looking for. We demonstrate that this method performs better than using just one method
like ORB, SIFT or FREAK, despite being fairly slower
Regularized Weighted Ensemble of Deep Classifiers ijcsa
Ensemble of classifiers increases the performance of the classification since the decision of many experts
are fused together to generate the resultant decision for prediction making. Deep learning is a classification algorithm where along with the basic learning technique, fine tuning learning is done for improved precision of learning. Deep classifier ensemble learning is having a good scope of research.Feature subset selection is another for creating individual classifiers to be fused for ensemble learning. All these ensemble techniques faces ill posed problem of overfitting. Regularized weighted ensemble of deep support vector machine performs the prediction analysis on the three UCI repository problems IRIS,Ionosphere and Seed data set, thereby increasing the generalization of the boundary plot between the
classes of the data set. The singular value decomposition reduced norm 2 regularization with the two level
deep classifier ensemble gives the best result in our experiments.
2D FEATURES-BASED DETECTOR AND DESCRIPTOR SELECTION SYSTEM FOR HIERARCHICAL R...gerogepatton
Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical classification is looking for. We demonstrate that this method performs better than using just one method
like ORB, SIFT or FREAK, despite being fairly slower.
2D FEATURES-BASED DETECTOR AND DESCRIPTOR SELECTION SYSTEM FOR HIERARCHICAL R...ijaia
Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical classification is looking for. We demonstrate that this method performs better than using just one method like ORB, SIFT or FREAK, despite being fairly slower
- What is Clustering, Honeypots and Density Based Clustering?
- What is Optics Clustering and how is it different than DB Clustering? …and how
can it be used for outlier detection.
- What is so-called soft clustering and how is it different than clustering? …and how
can it be used for outlier detection.
Data Tactics Data Science Brown Bag (April 2014)Rich Heimann
This is a presentation we perform internally every quarter as part of our Data Science Brown Bag Series. This presentation was talking about different types of soft clustering techniques - all of which the team currently performs depending on the complexity of the data and the complexity of customer problems. If you are interested in learning more about working with L-3 Data Tactics or interested in working for the L-3 Data Tactics Data Science team please contact us soon! Thank you.
A Survey on Cluster Based Outlier Detection Techniques in Data StreamIIRindia
In recent days, Data Mining (DM) is an emerging area of computational intelligence that provides new techniques, algorithms and tools for processing large volumes of data. Clustering is the most popular data mining technique today. Clustering used to separate a dataset into groups that finds intra-group similarity and inter-group similarity. Outlier detection (Anomaly) is to find small groups of data objects that are different when compared with rest of data. The outlier detection is an essential part of mining in data stream. Data Stream (DS) used to mine continuous arrival of high speed data Items. It plays an important role in the fields of telecommunication services, E-Commerce, Tracking customer behaviors and Medical analysis. Detecting outliers over data stream is an active research area. This survey presents the overview of fundamental outlier detection approaches and various types of outlier detection methods in data stream.
Similar to Introduction to Linear Discriminant Analysis (20)
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. 1. Who am I?
2. What is LDA?
3. Why use LDA?
4. How do I use
LDA?
https://memegenerator.net/instance/55587306/winter-is-coming-brace-yourself-powerpoint-slides-are-coming
6. Linear Discriminant
Analysis
A supervised dimensionality
reduction technique to be used
with continuous independent
variables and a categorical
dependent variables
A linear combination
of features
separates two or
more classes
Because it works
with numbers and
sounds science-y
7. More LDA
Ronald Fisher
Fisher’s Linear Discriminant (2-Class Method) - 1936
C R Rao
Multiple Discriminant Analysis - 1948
Also used
As a data classification technique
http://www.swlearning.com/quant/kohler/stat/biographical_sketches/Fisher_3.jpeg
http://www.buffalo.edu/ubreporter/archive/2011_07_21/rao_guy_medal.html
10. The curse of dimensionality
http://www.visiondummy.com/2014/04/curse-dimensionality-affect-classification/
Reducing the
number of
your features
might improve
your classifier
11. How much can I reduce my number of features?
One less than the number of labeled classes you have
My waterpoint dataset
1 2 3 4 5 6 87
Functional
Functional
Repair
NonFuctional
LDA2
My waterpoint dataset after LDA
LDA1
Functional
Functional
Repair
NonFuctional
3 Classes - 1 = 2 features (dimensions)
15. What you need to
start:
● Labeled data
● Ordinal features (the number
kind, not the category kind)
● Some idea about matrix algebra
● (Python)
https://www.memecenter.com/fun/330902/matrix--expectation-vs-reality
16. The Big Picture
Create a subspace that separates our classes really well, then we’ll project our
data onto that subspace.
Linear Discriminant:
To find that optimal subspace we’re going to:
● Minimize within-class variance (we want tight groups)
● Maximize the between-class distance (we want our groups as far apart as
possible)
32. The Math of Finding Linear
Discriminants
http://research.cs.tamu.edu/prism/lectures/pr/pr_l10.pdf
Working towards: We have so far:
Projected means
Scatter of the projection
33. The Math of Linear Discriminants
Fisher’s Criterion
Eigenvector
EigenvalueMatrix
35. 5 Steps to LDA
1) Means
2) Scatter Matrices
3) Finding Linear Discriminants
4) Subspace
5) Project Data
Iris Dataset
36. Step 4: Subspace
● Sort our Eigenvectors by
decreasing Eigenvalue
● Choose the top Eigenvectors to
make your transformation matrix
used to project your data
Choose top (Classes - 1) Eigenvalues
37. 5 Steps to LDA
1) Means
2) Scatter Matrices
3) Finding Linear Discriminants
4) Subspace
5) Project Data
Iris Dataset
38. Step 5: Project Data
X_lda = X.dot(W)
https://memegenerator.net/instance/48422270/willy-wonka-well-now-that-was-anticlimactic