In this chapter we report some Machine Learning (ML) and Pattern Recognition (PR) techniques applicable for classifying Stochastically Episodic (SE) events1. Researchers in the field of Pattern Recognition (PR) have traditionally presumed the availability of a representative set of data drawn from the classes of interest, say ω1 and ω2 in a 2-class problem. These samples are typically utilized in the development of the system's discriminant function. It is, however, widely recognized that there exists a particularly challenging class of PR problems for which a representative set is not available for the second class, which has motivated a great deal of research into the so-called domain of One Class (OC) classification. In this chapter, we primarily report the novel results found in [2, 4, 6], where we extend the frontiers of novelty detection by the introduction of a new field of problems open for analysis. In particular, we note that this new realm deviates from

Additional Metadata
Keywords Erroneous Data, Pattern Recognition, Rare Events, Stochastic Events
Persistent URL dx.doi.org/10.1007/978-3-642-28699-5_7
Series Smart Innovation, Systems and Technologies
Citation
Oommen, J, & Bellinger, C. (Colin). (2013). Emerging trends in Machine Learning: Classification of Stochastically Episodic events. Smart Innovation, Systems and Technologies. doi:10.1007/978-3-642-28699-5_7