In this chapter we present a two-dimensional representation of probabilities called likelihood spaces. In particular, we show the geometrical properties of Bayes' rule when projected into this two-dimensional space and extend this concept to Naieve Bayes classifiers. We apply this geometrical interpretation to a real machine learning problem of text categorisation and present a Web application that implements all the concepts on a standard text categorisation benchmark.
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