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{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{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{FeatureVectorEncoder}. These classes currently implement a form of feature hashing with multiple probes to limit feature ambiguity. @see{OnlineLogisticRegressionTest} @see{ContinuousValueEncoderTest} @see{TextValueEncoderTest} @see{WordLikeValueEncoderTest}