Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 66
... space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by the TLU weights wi , w2 , ... , Wd , Wd + 1 . Training a TLU to dichotomize ... Weight space,
... space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by the TLU weights wi , w2 , ... , Wd , Wd + 1 . Training a TLU to dichotomize ... Weight space,
Sivu 85
... weight vectors satisfying inequality ( 5.6 ) . That is , W.Y > 0 for all W ... space representation , it is clear that the boundaries of W ' are ... weight vector in the reduced weight - vector sequence Sŵ . That is | W - Ŵ , | 2 = W.W ...
... weight vectors satisfying inequality ( 5.6 ) . That is , W.Y > 0 for all W ... space representation , it is clear that the boundaries of W ' are ... weight vector in the reduced weight - vector sequence Sŵ . That is | W - Ŵ , | 2 = W.W ...
Sivu 137
... weight vector , 67 Sorting tasks , list of , 2 Spherical pattern clusters , 131 Stark , 125 , 126 Statistical ... space , 66 Weight vector , 66 Weight - vector sequence , 80 Weights , 16 Switching theory , 40 Symmetrical loss function ...
... weight vector , 67 Sorting tasks , list of , 2 Spherical pattern clusters , 131 Stark , 125 , 126 Statistical ... space , 66 Weight vector , 66 Weight - vector sequence , 80 Weights , 16 Switching theory , 40 Symmetrical loss function ...
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 |