Pattern Recognition and Application Video Lectures

Pattern Recognition and Application
'Pattern Recognition and Application' Video Lectures by Prof. P.K. Biswas from IIT Kharagpur
"Pattern Recognition and Application" - Video Lectures
1. Introduction
2. Feature Extraction - I
3. Feature Extraction - II
4. Feature Extraction - III
5. Bayes Decision Theory
6. Bayes Decision Theory (Contd.)
7. Normal Density and Discriminant Function
8. Normal Density and Discriminant Function (Contd.)
9. Bayes Decision Theory - Binary Features
10. Maximum Likelihood Estimation
11. Probability Density Estimation
12. Probability Density Estimation (Contd.)
13. Probability Density Estimation (Contd. )
14. Probability Density Estimation ( Contd.)
15. Probability Density Estimation ( Contd. )
16. Dimensionality Problem
17. Multiple Discriminant Analysis
18. Multiple Discriminant Analysis (Tutorial)
19. Multiple Discriminant Analysis (Tutorial )
20. Perceptron Criterion
21. Perceptron Criterion (Contd.)
22. MSE Criterion
23. Linear Discriminator (Tutorial)
24. Neural Networks for Pattern Recognition
25. Neural Networks for Pattern Recognition (Contd.)
26. Neural Networks for Pattern Recognition (Contd. )
27. RBF Neural Network
28. RBF Neural Network (Contd.)
29. Support Vector Machine
30. Hyperbox Classifier
31. Hyperbox Classifier (Contd.)
32. Fuzzy Min Max Neural Network for Pattern Recognition
33. Reflex Fuzzy Min Max Neural Network
34. Unsupervised Learning - Clustering
35. Clustering (Contd.)
36. Clustering using minimal spanning tree
37. Temporal Pattern recognition
38. Hidden Markov Model
39. Hidden Markov Model (Contd.)
40. Hidden Markov Model (Contd. )
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