Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 79
... theorem In this chapter we shall formally state and prove some theorems about the training procedures mentioned in Chapter 4. These theorems form the core of the theory of iterative nonparametric training methods , and their ...
... theorem In this chapter we shall formally state and prove some theorems about the training procedures mentioned in Chapter 4. These theorems form the core of the theory of iterative nonparametric training methods , and their ...
Sivu 81
... theorem can also be proved quite simply as a result of Theorem 5.1 . In the modified theorem we use an absolute error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error ...
... theorem can also be proved quite simply as a result of Theorem 5.1 . In the modified theorem we use an absolute error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error ...
Sivu 92
... theorem , we shall show that the sequence S✩ converges to a point P. For any fixed W in W let lim | Ŵ – W│ k ... theorem . 5.7 Bibliographical and historical remarks 6 3 4 The first proof of Theorem 5.1 was outlined by Rosenblatt.1 ...
... theorem , we shall show that the sequence S✩ converges to a point P. For any fixed W in W let lim | Ŵ – W│ k ... theorem . 5.7 Bibliographical and historical remarks 6 3 4 The first proof of Theorem 5.1 was outlined by Rosenblatt.1 ...
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 |