Java Machine Learning Library 0.1.3 - 2008-12-18

net.sf.javaml.classification Provides several classification algorithms.
net.sf.javaml.classification.evaluation Provides algorithms and measures to evaluate classification algorithms.
net.sf.javaml.classification.meta Provides meta-classifiers like for example the Bagging algorithm.
net.sf.javaml.classification.tree Provides classification trees and derivative algorithms like forests.
net.sf.javaml.clustering Provides algorithms to cluster data.
net.sf.javaml.clustering.evaluation Provides scores to evaluate the result of a clustering algorithm.
net.sf.javaml.clustering.mcl Provides an implementation of the MCL clustering algorithm.
net.sf.javaml.core The core of Java-ML: Instance and Dataset interfaces with their implementation can be found here.
net.sf.javaml.core.exception Provides exceptions that are used throughout Java-ML.
net.sf.javaml.core.kdtree Provides a KD-tree implementation for fast range- and nearest-neighbors-queries.
net.sf.javaml.distance Implements algorithms that can measure the distance, similarity or correlation between Instances.
net.sf.javaml.distance.dtw Provides DTW (dynamic time-warping) based distance measures.
net.sf.javaml.distance.fastdtw Provides the fast DTW (dynamic time-warping) approximation by Stan Salvador and Philip Chan.
net.sf.javaml.featureselection Provides algorithms to evaluation the worth of attributes and attribute sets.
net.sf.javaml.featureselection.ensemble Provides ensemble feature selection algorithms.
net.sf.javaml.filter Provides filters for instances and data sets.
net.sf.javaml.filter.discretize Provides filters that can discretize continuous values
net.sf.javaml.filter.instance Filters that can be run on a single instance at once.
net.sf.javaml.filter.missingvalue Contains filters that can be used to replace missing values with more sensible information.
net.sf.javaml.filter.normalize Provides filters to normalize values.
net.sf.javaml.matrix Implements a matrix which can be backed by a memory- or file-based array. Provides useful tools for the library. Provides tools to load and data sets to various sources and destinations Provides Weka tools: converting to data to Weka, wrappers for algorithms, etc.
net.sf.javaml.utils Provides utilities for algorithms; this package will disappear at some point!
tutorials.classification Provides tutorials for the Classifier interface and the associated classes for evaluation and performance assessment.
tutorials.clustering Provides tutorials for the Clustering interface.
tutorials.core Provides tutorials for the Instance and Dataset core interfaces.
tutorials.featureselection Provides tutorials for the tools package.


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