Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
Sivu 11
... chapters In the next chapter we discuss several families of discriminant functions as possible candidates for use in a pattern - classifying machine . We exam- ine the properties of some in detail , and present block diagrams to suggest ...
... chapters In the next chapter we discuss several families of discriminant functions as possible candidates for use in a pattern - classifying machine . We exam- ine the properties of some in detail , and present block diagrams to suggest ...
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 79
... 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 iterative nonparametric training methods , and their consequences are applicable ...
... 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 iterative nonparametric training methods , and their consequences are applicable ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |