Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 11
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 block ...
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 block ...
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 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 at
a ...
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 at
a ...
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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 |