/**
* <p>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.</p>
*
* <p>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.</p>
*
* <p>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.</p>
*/
package org.apache.mahout.classifier.sgd;