Lecture 32 of 43Feature Selection
Feature Selection Criteria Function: Probabilistic Separability Based
Prof. Sukhendu Das and Prof. C.A. Murthy · IIT Madras
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- Pattern Recognition, IIT Madras (at NPTEL)
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- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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All the lectures
- Principles of Pattern Recognition I (Introduction and Uses)
- Principles of Pattern Recognition II (Mathematics)
- Principles of Pattern Recognition III (Classification and Bayes Decision Rule)
- Clustering vs. Classification
- Relevant Basics of Linear Algebra, Vector Spaces
- Eigen Value and Eigen Vectors
- Vector Spaces
- Rank of Matrix and SVD
- Types of Errors
- Examples of Bayes Decision Rule
- Normal Distribution and Parameter Estimation
- Training Set, Test Set
- Standardization, Normalization, Clustering and Metric Space
- Normal Distribution and Decision Boundaries I
- Normal Distribution and Decision Boundaries II
- Bayes Theorem
- Linear Discriminant Function and Perceptron
- Perceptron Learning and Decision Boundaries
- Linear and Non-Linear Decision Boundaries
- K-NN Classifier
- Principal Component Analysis (PCA)
- Fisher’s LDA
- Gaussian Mixture Model (GMM)
- Assignments
- Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria.
- K-Means Algorithm and Hierarchical Clustering
- K-Medoids and DBSCAN
- Feature Selection : Problem statement and Uses
- Feature Selection : Branch and Bound Algorithm
- Feature Selection : Sequential Forward and Backward Selection
- Cauchy Schwartz Inequality
- Feature Selection Criteria Function: Probabilistic Separability Based
- Feature Selection Criteria Function: Interclass Distance Based
- Principal Components
- Comparison Between Performance of Classifiers
- Basics of Statistics, Covariance, and their Properties
- Data Condensation, Feature Clustering, Data Visualization
- Probability Density Estimation
- Visualization and Aggregation
- Support Vector Machine (SVM)
- FCM and Soft-Computing Techniques
- Examples of Uses or Application of Pattern Recognition; And When to do clustering
- Examples of Real-Life Dataset