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 28
... decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric surfaces . Specifically , if R , and R , share a common boundary , it is a section of the surface S1 ...
... decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric surfaces . Specifically , if R , and R , share a common boundary , it is a section of the surface S1 ...
Sivu 119
... decision surface which minimizes the probability of error is a hyper- plane perpendicular to the line segment joining the means of the two density functions . It should be observed that if the two density func- tions overlap ...
... decision surface which minimizes the probability of error is a hyper- plane perpendicular to the line segment joining the means of the two density functions . It should be observed that if the two density func- tions overlap ...
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 |