Virtual University

Model-Based Methods: Self-Organizing Feature Map (SOM) (V-U)

Partitioning Methods: Basic Concept (V-U)

Multi layered Meta-web Architecture (V-U)

Partitioning Methods: Discussion/Comments on the K-Means Method (V-U)

N gram probabilities (V-U)

Partitioning Methods: The K-Means Clustering Method (V-U)

Naive bayes advantages (V-U)

Partitioning Methods: The K-Medoids Clustering Method (V-U)

Naive Bayes and LM (V-U)

Partitioning Methods: Variations of the K-Means Method (V-U)

Rain forest example (V-U)

Ratio scaled variables (V-U)

Regular expressions (V-U)

Rule-Based Classification: Rule Induction: Sequential Covering Method (V-U)

Rule-Based Classification: Using IF-THEN Rules for Classification (V-U)

Rule-Based Classification:Rule Extraction from a Decision Tree (V-U)

Spell correction (V-U)

Stages of NLP (V-U)

Support Vector Machines: Background and Applications (V-U)

Support Vector Machines: General Philosophy (V-U)

Support Vector Machines: Introduction (V-U)

Syntax processing (V-U)

WEKA Clustering (V-U)

Techniques to Improve Classification Accuracy: Ensemble Methods: Bagging: Bootstrap Aggregation (V-U)

WEKA Features (V-U)