Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 31
Sivu 19
573 S , ( redundant ) sa R 23 FIGURE 2 · 2 Examples of decision regions and
surfaces resulting from linear discriminant functions p - - RER | $ 23 FIGURE 2 . 3
Decision regions for a minimum - distance classifier with respect to the points P1
...
573 S , ( redundant ) sa R 23 FIGURE 2 · 2 Examples of decision regions and
surfaces resulting from linear discriminant functions p - - RER | $ 23 FIGURE 2 . 3
Decision regions for a minimum - distance classifier with respect to the points P1
...
Sivu 68
Thus , the solution region and the solution weight point W indicated in the figure
apply to the linear dichotomy for which augmented patterns number one , two ,
and three all belong to the set Y1 . Each of these patterns would cause a TLU ...
Thus , the solution region and the solution weight point W indicated in the figure
apply to the linear dichotomy for which augmented patterns number one , two ,
and three all belong to the set Y1 . Each of these patterns would cause a TLU ...
Sivu 73
3 A plane which correctly partitions eight three - dimensional patterns Response
Threshold element + 1 - y = Augmented pattern Weights FIGURE 4 . 4 A TLU
trained to respond correctly to eight three - dimensional patterns fourth iteration
after ...
3 A plane which correctly partitions eight three - dimensional patterns Response
Threshold element + 1 - y = Augmented pattern Weights FIGURE 4 . 4 A TLU
trained to respond correctly to eight three - dimensional patterns fourth iteration
after ...
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Sisältö
Preface vii | 7 |
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 gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix 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
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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |