Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 22
Sivu 99
... training procedure for committee machines Suppose that we have training pattern subsets Y1 and Y2 , comprising the training set Y , and we wish to find a committee machine of size P to separate these subsets . To accomplish this , we ...
... training procedure for committee machines Suppose that we have training pattern subsets Y1 and Y2 , comprising the training set Y , and we wish to find a committee machine of size P to separate these subsets . To accomplish this , we ...
Sivu 119
... training subsets . If we were willing to assume initially that these distributions were normal , then the parametric training methods outlined in Chapter 3 would lead to a decision surface closely approximating the optimum sur- face if ...
... training subsets . If we were willing to assume initially that these distributions were normal , then the parametric training methods outlined in Chapter 3 would lead to a decision surface closely approximating the optimum sur- face if ...
Sivu 121
... training subsets must be computed . If these computations are to be performed rapidly , each of the training patterns must be stored ( as weight vectors , for example ) in some rapid - access memory . Because the method works best when ...
... training subsets must be computed . If these computations are to be performed rapidly , each of the training patterns must be stored ( as weight vectors , for example ) in some rapid - access memory . Because the method works best when ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |