Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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This set of patterns is called the training set . The desired classifications of these
patterns are assumed to be known . Discriminant functions are then chosen , by
methods to be discussed in general below and more specifically later , which ...
This set of patterns is called the training set . The desired classifications of these
patterns are assumed to be known . Discriminant functions are then chosen , by
methods to be discussed in general below and more specifically later , which ...
Sivu 11
Chapter 3 will investigate decision - theoretic parametric training methods . The
mathematical foundation underlying these training methods seems to be more
extensive than the theory supporting the nonparametric training methods . On the
...
Chapter 3 will investigate decision - theoretic parametric training methods . The
mathematical foundation underlying these training methods seems to be more
extensive than the theory supporting the nonparametric training methods . On the
...
Sivu 65
CHAPTER 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 methods for linear machines (
employing ...
CHAPTER 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 methods for linear machines (
employing ...
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Sisältö
Preface vii | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative 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 Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
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 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |