  The proliferation of sensor devices monitoring human activity generates
voluminous amount of temporal sequences needing to be interpreted and
categorized. Moreover, complex behavior detection requires the personalization
of multi-sensor fusion algorithms. Conditional random fields (CRFs) are
commonly used in structured prediction tasks such as part-of-speech tagging in
natural language processing. Conditional probabilities guide the choice of each
tag/label in the sequence conflating the structured prediction task with the
sequence classification task where different models provide different
categorization of the same sequence. The claim of this paper is that CRF models
also provide discriminative models to distinguish between types of sequence
regardless of the accuracy of the labels obtained if we calibrate the class
membership estimate of the sequence. We introduce and compare different neural
network based linear-chain CRFs and we present experiments on two complex
sequence classification and structured prediction tasks to support this claim.
