Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 39
Sivu 7
... classifier method will produce a more detailed functional block diagram of the basic model for a pattern classifier discussed in Sec . 1.2 . Our discriminant- function pattern classifier , illustrated in Fig . 1.4 , would employ R dis ...
... classifier method will produce a more detailed functional block diagram of the basic model for a pattern classifier discussed in Sec . 1.2 . Our discriminant- function pattern classifier , illustrated in Fig . 1.4 , would employ R dis ...
Sivu 9
... classifier whose discriminant functions can be obtained by training is called a trainable pattern classifier . 1.7 Training methods In this book we shall discuss examples of two types of training methods , parametric and nonparametric ...
... classifier whose discriminant functions can be obtained by training is called a trainable pattern classifier . 1.7 Training methods In this book we shall discuss examples of two types of training methods , parametric and nonparametric ...
Sivu 24
... classifier with respect to point sets . i = ངག • Suppose we are given R finite point sets P1 , P2 , . . . , PR . For each .. , R , let the ith point set consist of the L ; points P , ( 1 ) , P , ( 2 ) , P ( L ) . Let us define the ...
... classifier with respect to point sets . i = ངག • Suppose we are given R finite point sets P1 , P2 , . . . , PR . For each .. , R , let the ith point set consist of the L ; points P , ( 1 ) , P , ( 2 ) , P ( L ) . Let us define the ...
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 |