Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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1.8 Summary of book by chapters In the next chapter we discuss several families of discriminant functions as possible candidates for use in a pattern - classifying machine . We examine the properties of some in detail , and present ...
1.8 Summary of book by chapters In the next chapter we discuss several families of discriminant functions as possible candidates for use in a pattern - classifying machine . We examine the properties of some in detail , and present ...
Sivu 79
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 the theory of ...
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 the theory of ...
Sivu 116
The weather - prediction example of Chapter 1 can be used to illustrate such a multimodal task . Suppose that a pattern - classifying machine is to be used to classify weather measurements into two categories : Those which precede fog ...
The weather - prediction example of Chapter 1 can be used to illustrate such a multimodal task . Suppose that a pattern - classifying machine is to be used to classify weather measurements into two categories : Those which precede fog ...
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable negative 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 selected separable shown side space specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |