Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number ...
Sivu 19
... Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In many cases some of the hyperplanes defined by Eq . ( 2 · 8 ) are not actually used as decision surfaces . The hyperplane S , is not used ...
... Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In many cases some of the hyperplanes defined by Eq . ( 2 · 8 ) are not actually used as decision surfaces . The hyperplane S , is not used ...
Sivu 20
... decision regions and surfaces for the minimum - distance classifier with respect to the two - dimensional points P1 , P2 , and P3 . We note in the examples of Figs . 2-2 and 2-3 that the decision regions are convex ( a region is convex ...
... decision regions and surfaces for the minimum - distance classifier with respect to the two - dimensional points P1 , P2 , and P3 . We note in the examples of Figs . 2-2 and 2-3 that the decision regions are convex ( a region is convex ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |