Data Mining: Concepts and Techniques
6.7 Support Vector Machines 337 6.7.1 The Case When the Data Are Linearly Separable 337 6.7.2 The Case When the Data Are Linearly Inseparable 342 6.8 Associative Classification: Classification by Association Rule Analysis 344 6.9 Lazy Learners (or Learning from Your Neighbors) 347 6.9.1 k-Nearest-Neighbor Classifiers 348 6.9.2 Case-Based ...
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Data Mining: Concepts and Techniques
hanj.cs.illinois.edu2.4 Data Integration and Transformation 67 2.4.1 Data Integration 67 2.4.2 Data Transformation 70 2.5 Data Reduction 72 2.5.1 Data Cube Aggregation 73 2.5.2 Attribute Subset Selection 75 2.5.3 Dimensionality Reduction 77 2.5.4 Numerosity Reduction 80 2.6 Data Discretization and Concept Hierarchy Generation 86
2.4.8 Kullback-Leibler Divergence
hanj.cs.illinois.edufrom an observed frequency distribution, as illustrate in the following example. Example 2.24. Computing the KL Divergence by Smoothing. Sup-pose there are two sample distributions P and Q as follows: P: (a: 3/5,b:
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