Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 27
Sivu 79
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 88
Theorem 5.2 Y2 , · • Let the training subsets Y1 , 2 , ... , YR be linearly separable . Let Sw , Sw ,, . . . , SwR be the weight - vector sequences generated by any training sequence Sy , using the generalized fixed - increment error ...
Theorem 5.2 Y2 , · • Let the training subsets Y1 , 2 , ... , YR be linearly separable . Let Sw , Sw ,, . . . , SwR be the weight - vector sequences generated by any training sequence Sy , using the generalized fixed - increment error ...
Sivu 90
5.6 A related training theorem for the case R = 2 In Chapter 4 we discussed another error - correction procedure , the frac- tional correction rule . In this rule the correction increment at the kth step ck is set equal to that value ...
5.6 A related training theorem for the case R = 2 In Chapter 4 we discussed another error - correction procedure , the frac- tional correction rule . In this rule the correction increment at the kth step ck is set equal to that value ...
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
2 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 |