Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 40
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... 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 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 ...
Sivu 115
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. CHAPTER 7 PIECEWISE LINEAR MACHINES 7.1 Multimodal pattern - classifying tasks Piecewise linear ( PWL ) machines were originally defined in Chapter 2 . The general ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. CHAPTER 7 PIECEWISE LINEAR MACHINES 7.1 Multimodal pattern - classifying tasks Piecewise linear ( PWL ) machines were originally defined in Chapter 2 . The general ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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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 |