Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 37
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 . tienn 1 datace discriminants , not any date 1.7 Training methods " In this book we shall discuss examples of two types of ...
... classifier whose discriminant functions can be obtained by training is called a trainable pattern classifier . tienn 1 datace discriminants , not any date 1.7 Training methods " In this book we shall discuss examples of two types of ...
Sivu 24
... classifier with respect to point sets . Suppose we are given R finite point sets P1 , P2 , i = 1 , · " ... " PR . " For each R , let the ith point set consist of the L points P , ( 1 ) , P , ( 2 ) , P ( L ) . Let us define the Euclidean ...
... classifier with respect to point sets . Suppose we are given R finite point sets P1 , P2 , i = 1 , · " ... " PR . " For each R , let the ith point set consist of the L points P , ( 1 ) , P , ( 2 ) , P ( L ) . Let us define the Euclidean ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |