A probabilistic examplar based model
Rodriguez Martinez, AF
A central problem in case based reasoning (CBR) is how to store and retrieve\ud cases. One approach to this problem is to use exemplar based models, where only\ud the prototypical cases are stored. However, the development of an exemplar based\ud model (EBM) requires the solution of several problems: (i) how can a EBM be\ud represented? (ii) given a new case, how can a suitable exemplar be retrieved? (iii)\ud what makes a good exemplar? (iv) how can an EBM be learned incrementally?\ud This thesis develops a new model, called a probabilistic exemplar based model,\ud that addresses these research questions. The model utilizes Bayesian networks\ud to develop a suitable representation and uses probability theory to develop the\ud foundations of the developed model. A probability propagation method is used\ud to retrieve exemplars when a new case is presented and for assessing the prototypicality\ud of an exemplar.\ud The model learns incrementally by revising the exemplars retained and by\ud updating the conditional probabilities required by the Bayesian network. The\ud problem of ignorance, encountered when only a few cases have been observed,\ud is tackled by introducing the concept of a virtual exemplar to represent all the\ud unseen cases.\ud The model is implemented in C and evaluated on three datasets. It is also\ud contrasted with related work in CBR and machine learning (ML).