Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 20
Sivu 6
... plane . Note that the decision surfaces in the x1 , x2 plane are given by the projections of the intersections of the discriminant functions . Of course , the location and form 6 TRAINABLE PATTERN CLASSIFIERS Discriminant functions,
... plane . Note that the decision surfaces in the x1 , x2 plane are given by the projections of the intersections of the discriminant functions . Of course , the location and form 6 TRAINABLE PATTERN CLASSIFIERS Discriminant functions,
Sivu 71
... plane . For λ = 0 , the weight point is not moved at all ; for λ = 1 , the weight point is moved to the pattern ... plane ( e.g. , across it ) , and then the next pattern in the set is examined . The process continues until a solution ...
... plane . For λ = 0 , the weight point is not moved at all ; for λ = 1 , the weight point is moved to the pattern ... plane ( e.g. , across it ) , and then the next pattern in the set is examined . The process continues until a solution ...
Sivu 107
... plane is normal to the vector ( 1,1,1 ) , and the TLU in the second layer which implements this plane gives equal weight to each of the three outputs from the first - layer TLUs . That is , this particular two - layer machine is a ...
... plane is normal to the vector ( 1,1,1 ) , and the TLU in the second layer which implements this plane gives equal weight to each of the three outputs from the first - layer TLUs . That is , this particular two - layer machine is a ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |