Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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CHAPTER 5 CHAPTER TRAINING THEOREMS 5 : 1 The fundamental training
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 ...
CHAPTER 5 CHAPTER TRAINING THEOREMS 5 : 1 The fundamental training
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 ...
Sivu 92
38 ) for all W in W . We therefore say that WK + 1 is pointwise closer than û to W .
As a first step in proving the theorem , we shall show that the sequence Sû
converges to a point P . For any fixed W in W let lim Wk – W = 1 ( W ) ; ( W ) exists
...
38 ) for all W in W . We therefore say that WK + 1 is pointwise closer than û to W .
As a first step in proving the theorem , we shall show that the sequence Sû
converges to a point P . For any fixed W in W let lim Wk – W = 1 ( W ) ; ( W ) exists
...
Sivu 93
Block has proved a generalized version of Theorem 5 . 1 in which the correction
increment Ck of Eq . ( 5 . 4 ) need not be independent of k . Theorem 5•2 was first
proved by C . Kesler at Cornell University . Our proof is a version of Kesler ' s as ...
Block has proved a generalized version of Theorem 5 . 1 in which the correction
increment Ck of Eq . ( 5 . 4 ) need not be independent of k . Theorem 5•2 was first
proved by C . Kesler at Cornell University . Our proof is a version of Kesler ' s as ...
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Sisältö
Preface vii | 7 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative 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 solution space Stanford step 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 |
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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |