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A probabilistic examplar based model

Rodriguez Martinez, AF


AF Rodriguez Martinez



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


Rodriguez Martinez, A. A probabilistic examplar based model. (Thesis). University of Salford

Thesis Type Thesis
Deposit Date Sep 22, 2011
Publicly Available Date Sep 22, 2011
Award Date Jan 1, 1998


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