Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 79
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 81
Theorem 5 . 1 Let the training subsets Yi and Y2 be linearly separable . Let Sw
be the weight - vector sequence generated by any training sequence Sy , using
the fixed - increment error - correction procedure and beginning with any initial ...
Theorem 5 . 1 Let the training subsets Yi and Y2 be linearly separable . Let Sw
be the weight - vector sequence generated by any training sequence Sy , using
the fixed - increment error - correction procedure and beginning with any initial ...
Sivu 93
Block has proved a generalized version of Theorem 5 . 1 in which the correction
increment Cx 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 Cx 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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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 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 networks 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 specific 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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |