Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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This set of patterns is called the training set . The desired classifications / sli 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 / sli 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 76
6 Applications to o machines The training methods that have been discussed in
this chapter have all been phrased to apply to linear machines . We can easily
observe that these same methods can be used to train machines . From Chapter
2 ...
6 Applications to o machines The training methods that have been discussed in
this chapter have all been phrased to apply to linear machines . We can easily
observe that these same methods can be used to train machines . From Chapter
2 ...
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Sisältö
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
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 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 mean vector measurements 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 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 |