Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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CHAPTER 5 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 the theory of ...
CHAPTER 5 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 the theory of ...
Sivu 92
that if 0 < ≤ 2 , then - WIIW - k WI ( 5.38 ) k for all W in W. We therefore say that Ŵ + 1 is pointwise closer than Ŵx to W. As a first step in proving the theorem , we shall show that the sequence S converges to a point P. - k For ...
that if 0 < ≤ 2 , then - WIIW - k WI ( 5.38 ) k for all W in W. We therefore say that Ŵ + 1 is pointwise closer than Ŵx to W. As a first step in proving the theorem , we shall show that the sequence S converges to a point P. - k For ...
Sivu 93
Theorem 5.2 was first proved by C. Kesler at Cornell University . Our proof is a version of Kesler's as it was related to the author during discussions in July , 1963 . 8 Theorem 5.3 is a slightly modified version of a theorem by ...
Theorem 5.2 was first proved by C. Kesler at Cornell University . Our proof is a version of Kesler's as it was related to the author during discussions in July , 1963 . 8 Theorem 5.3 is a slightly modified version of a theorem by ...
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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 step subsidiary discriminant Suppose terns 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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |