Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 20
2 and 2 - 3 that the decision regions are convex ( a region is convex if and only if
the straight - line segment connecting two ... It will be left as an exercise for the
reader to verify that the decision regions of a linear machine are always convex .
2 and 2 - 3 that the decision regions are convex ( a region is convex if and only if
the straight - line segment connecting two ... It will be left as an exercise for the
reader to verify that the decision regions of a linear machine are always convex .
Sivu 67
Therefore , each region in weight space corresponds to a different linear
dichotomy of the N patterns , and , conversely , a ... For any given linear
dichotomy , the corre* If we count the number of regions in weight space formed
by N augmented ...
Therefore , each region in weight space corresponds to a different linear
dichotomy of the N patterns , and , conversely , a ... For any given linear
dichotomy , the corre* If we count the number of regions in weight space formed
by N augmented ...
Sivu 68
sponding region in weight space is called the solution region . It is a convex
region containing all of the solution weight points W satisfying inequality ( 4 : 3 ) .
These ideas are illustrated in Fig . 4 : 1 for a two - dimensional weight space ( D =
2 ) ...
sponding region in weight space is called the solution region . It is a convex
region containing all of the solution weight points W satisfying inequality ( 4 : 3 ) .
These ideas are illustrated in Fig . 4 : 1 for a two - dimensional weight space ( D =
2 ) ...
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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 |