Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 11
... nonparametric methods . In Chapter 4 we begin our discussion of examples of some of the nonparametric training methods . There we shall introduce some of the more important training algorithms currently in use . The presentation will be ...
... nonparametric methods . In Chapter 4 we begin our discussion of examples of some of the nonparametric training methods . There we shall introduce some of the more important training algorithms currently in use . The presentation will be ...
Sivu 65
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. CHAPTER 4 SOME NONPARAMETRIC TRAINING METHODS FOR MACHINES 4.1 Nonparametric training of a TLU In this chapter we shall introduce some specific nonparametric training ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. CHAPTER 4 SOME NONPARAMETRIC TRAINING METHODS FOR MACHINES 4.1 Nonparametric training of a TLU In this chapter we shall introduce some specific nonparametric training ...
Sivu 119
... nonparametric procedures are being studied for application to " overlapping " pattern subsets . In the next few sections we shall develop some nonparametric procedures for training PWL machines that do not depend on the error ...
... nonparametric procedures are being studied for application to " overlapping " pattern subsets . In the next few sections we shall develop some nonparametric procedures for training PWL machines that do not depend on the error ...
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 |