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Computer Science and Engineering
Pattern Recognition (Web)
Syllabus
Co-ordinated by :
IISc Bangalore
Available from :
2012-01-02
Lec :
1
Modules / Lectures
Introduction
Background
Introduction
Paradigms for Pattern Recognition
Statistical Pattern Recognition
Representation of Patterns and Classes
Different Representation Schemes
Tree-Based Representations
Metric and Non-Metric Proximity Measures
Dissimilarity Measures
Distance Between Pattern Collections
Feature Extraction
Fisher’s Discriminant
Principal Components as Features
Different Approaches to Feature Selection
Branch and Bound Schemes
Sequential Feature Selection
Nearest Neighbour Classifier and its variants
Nearest Neighbour Classifier
Soft Nearest Neighbour Classifiers
Efficient Algorithms for Nearest Neighbour Classification
Efficient Nearest Neighbour Classifier
Ordered Partitions
Different approaches to Prototype Selection
Minimal Distance Classifier
Condensed Nearest Neighbour Classifier
Modified Condensed Nearest Neighbour Classifier
Bayes Classifier
Bayes Classifier
Naive Bayes Classifier
Bayesian Belief Networks
Decision Trees
Introduction to Decision Trees
Construction of Decision Trees
Axis-Parallel and Oblique Decision Trees
Learning Decision Trees
Linear Discriminant Functions
Introduction to Discriminant Functions
Characterization of the Decision Boundary
Learning the Discriminant Function
Support Vector Machines
Introduction to Support Vector Machines
Training Support Vector Machines
Clustering
What is Clustering?
Representation of Patterns and Clusters
Clustering Process
Clustering Algorithms
Clustering Large Datasets
Incremental Clustering
Divide-and-Conquer Clustering
Combination of Classifiers
Introduction to Combining Classifiers
AdaBoost for Classification
Schemes for Combining Classifiers
Combination of Homogeneous Classifiers
Application - Document Recognition
Document Processing
Document Classification and Retrieval
Web Content
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