Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 46
Sivu 16
... values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ...
... values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ...
Sivu 44
... values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 , • " R. " In such a ... values of the parameters of the p ( X | i ) and of the parameters p ( i ) . 2. The values of the parameters of the p ...
... values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 , • " R. " In such a ... values of the parameters of the p ( X | i ) and of the parameters p ( i ) . 2. The values of the parameters of the p ...
Sivu 49
... values of the a priori proba- bilities p ( 1 ) and 1 p ( 1 ) affect only the value of wa + 1 . As category 1 becomes less probable a priori , wa + 1 decreases . Such a decrease of wa + 1 favors a category 2 response for all patterns ...
... values of the a priori proba- bilities p ( 1 ) and 1 p ( 1 ) affect only the value of wa + 1 . As category 1 becomes less probable a priori , wa + 1 decreases . Such a decrease of wa + 1 favors a category 2 response for all patterns ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
2 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
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 |