Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 92
Sivu 84
... function of the family is defined as the family's ability to meet the psychologic needs of family members . These needs include affection and understanding . This ... Family functions can 84 UNITI Basic Concepts in Community - Based Nursing.
... function of the family is defined as the family's ability to meet the psychologic needs of family members . These needs include affection and understanding . This ... Family functions can 84 UNITI Basic Concepts in Community - Based Nursing.
Sivu 116
... function family used by the checker . Here , t ' , q ' , e ' are determined by t , q , e . So , if you consider the function family to be ( t ' , q ' , ' ) - secure , you can immediately derive the checker's security level exactly . 2 ...
... function family used by the checker . Here , t ' , q ' , e ' are determined by t , q , e . So , if you consider the function family to be ( t ' , q ' , ' ) - secure , you can immediately derive the checker's security level exactly . 2 ...
Sivu 170
... function family . Because there might be two teams with the same function family , each team's combined cards must include at most one of each of the following representations : graph , equation , table , and children's story or real ...
... function family . Because there might be two teams with the same function family , each team's combined cards must include at most one of each of the following representations : graph , equation , table , and children's story or real ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |