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/**
 * 

Implements a variety of on-line logistric regression classifiers using SGD-based algorithms. * SGD stands for Stochastic Gradient Descent and refers to a class of learning algorithms * that make it relatively easy to build high speed on-line learning algorithms for a variety * of problems, notably including supervised learning for classification.

* *

The primary class of interest in the this package is * {@link org.apache.mahout.classifier.sgd.CrossFoldLearner} which contains a * number (typically 5) of sub-learners, each of which is given a different portion of the * training data. Each of these sub-learners can then be evaluated on the data it was not * trained on. This allows fully incremental learning while still getting cross-validated * performance estimates.

* *

The CrossFoldLearner implements {@link org.apache.mahout.classifier.OnlineLearner} * and thus expects to be fed input in the form * of a target variable and a feature vector. The target variable is simply an integer in the * half-open interval [0..numFeatures) where numFeatures is defined when the CrossFoldLearner * is constructed. The creation of feature vectors is facilitated by the classes that inherit * from {@link org.apache.mahout.vectorizer.encoders.FeatureVectorEncoder}. * These classes currently implement a form of feature hashing with * multiple probes to limit feature ambiguity.

*/ package org.apache.mahout.classifier.sgd;




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