Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 51
Sivu 72
An extremely simple graphical example of error - correction training is illustrated
in Fig . 4 . 2 . There are four patterns represented by pattern hyperplanes in
weight space . The small arrows attached to these planes in this case indicate the
...
An extremely simple graphical example of error - correction training is illustrated
in Fig . 4 . 2 . There are four patterns represented by pattern hyperplanes in
weight space . The small arrows attached to these planes in this case indicate the
...
Sivu 101
4 we will illustrate this training procedure for an example in which we have three
augmented patterns of two dimensions . 6 : 4 An example The training procedure
described above can be illustrated quite clearly by a two - dimensional example ...
4 we will illustrate this training procedure for an example in which we have three
augmented patterns of two dimensions . 6 : 4 An example The training procedure
described above can be illustrated quite clearly by a two - dimensional example ...
Sivu 105
For example , patterns 3 and 7 both yield a response of + 1 for TLU 1 and a
response of - 1 for the other TLUs ; hence these two patterns are transformed into
the single point ( 1 , - 1 , - 1 ) in image space . The numbers associated with the ...
For example , patterns 3 and 7 both yield a response of + 1 for TLU 1 and a
response of - 1 for the other TLUs ; hence these two patterns are transformed into
the single point ( 1 , - 1 , - 1 ) in image space . The numbers associated with the ...
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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 |