Java Machine Learning Library 0.1.7

Packages 
Package Description
libsvm  
net.sf.javaml.classification
Provides several classification algorithms.
net.sf.javaml.classification.bayes  
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.distance.fastdtw.dtw  
net.sf.javaml.distance.fastdtw.lang  
net.sf.javaml.distance.fastdtw.matrix  
net.sf.javaml.distance.fastdtw.timeseries  
net.sf.javaml.distance.fastdtw.util  
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.featureselection.ranking
Provides feature ranking algorithms.
net.sf.javaml.featureselection.scoring
Provides feature scoring algorithms.
net.sf.javaml.featureselection.subset
Provides feature selection tutorials.
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.
net.sf.javaml.sampling  
net.sf.javaml.tools
Provides useful tools for the library.
net.sf.javaml.tools.data
Provides tools to load and data sets to various sources and destinations.
net.sf.javaml.tools.weka
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  
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 ensemble feature selection algorithms.
tutorials.filter  
tutorials.tools
Provides tutorials for the tools package.

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