Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 16
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 106
... plane shown is one which separates these subsets . Thus a two - layer machine with three TLUS in the first layer is adequate for dichotomizing the given pattern subsets . The plane shown in Fig . 6.76 is normal to the major diagonal of ...
... plane shown is one which separates these subsets . Thus a two - layer machine with three TLUS in the first layer is adequate for dichotomizing the given pattern subsets . The plane shown in Fig . 6.76 is normal to the major diagonal of ...
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 |
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 |