Example usage for weka.attributeSelection UnsupervisedAttributeEvaluator subclass-usage

List of usage examples for weka.attributeSelection UnsupervisedAttributeEvaluator subclass-usage

Introduction

In this page you can find the example usage for weka.attributeSelection UnsupervisedAttributeEvaluator subclass-usage.

Usage

From source file PrincipalComponents.java

/**
 * <!-- globalinfo-start --> Performs a principal components analysis and
 * transformation of the data. Use in conjunction with a Ranker search.
 * Dimensionality reduction is accomplished by choosing enough eigenvectors to
 * account for some percentage of the variance in the original data---default
 * 0.95 (95%). Attribute noise can be filtered by transforming to the PC space,

From source file data.generation.target.utils.PrincipalComponents.java

/**
 <!-- globalinfo-start -->
 * Performs a principal components analysis and transformation of the data. Use in conjunction with a Ranker search. Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data---default 0.95 (95%). Attribute noise can be filtered by transforming to the PC space, eliminating some of the worst eigenvectors, and then transforming back to the original space.
 * <p/>
 <!-- globalinfo-end -->
 *

From source file HomeWork7.PrincipalComponents.java

/**
 * <!-- globalinfo-start --> Performs a principal components analysis and
 * transformation of the data. Use in conjunction with a Ranker search.
 * Dimensionality reduction is accomplished by choosing enough eigenvectors to
 * account for some percentage of the variance in the original data---default
 * 0.95 (95%). Attribute noise can be filtered by transforming to the PC space,

From source file hw7.PrincipalComponents.java

/**
 * <!-- globalinfo-start --> Performs a principal components analysis and
 * transformation of the data. Use in conjunction with a Ranker search.
 * Dimensionality reduction is accomplished by choosing enough eigenvectors to
 * account for some percentage of the variance in the original data---default
 * 0.95 (95%). Attribute noise can be filtered by transforming to the PC space,