By Nils J Nilsson
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The algorithm is stopped after some chosen time t . . Examples of training curves for TLU's performance on training set performance on test set cumulative number of corrections. CHAPTER 4. NEURAL NETWORKS 50 After stopping, the weights in the pocket are used as a set that should give a small number of errors on the training set. Error-correction proceeds as usual with the ordinary set of weights. Also see methods proposed by John, 1995] and by Marchand & Golea, 1993]. The latter is claimed to outperform the The natural generalization of a (two-category) TLU to an R-category classi er is the structure, shown in Fig.
Selecting a hypothesis from the version space can be thought of as a search problem. One can start with a very general function and specialize it through various specialization operators until one nds a function that is consistent (or adequately so) with a set of training patterns. Such procedures are usually called top-down methods. Or, one can start with a very special function and generalize it|resulting in bottom-up methods. We shall see instances of both styles of learning in this book. 4 The Candidate Elimination Method The candidate elimination method, is an incremental method for computing the boundary sets.
Relate to term learning algorithm presented in Chapter Two. Also discuss best- rst search methods. 4. ] Selecting a hypothesis from the version space can be thought of as a search problem. One can start with a very general function and specialize it through various specialization operators until one nds a function that is consistent (or adequately so) with a set of training patterns. Such procedures are usually called top-down methods. Or, one can start with a very special function and generalize it|resulting in bottom-up methods.