Adaptive classification

We present an online learning system that is capable of analyzing an input-output data sequence to construct a sequence of binary classifications, without being provided correct class information as part of the training process. The system employs a combination of supervised and unsupervised learning techniques to form two or more behavior models. By examining these models for consistency with the sequence of observed data, an estimate of the class at each time step can be constructed.