Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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While there is only a scanty mathematical understanding of these machines , two complementary viewpoints are discussed which aid formulation of meaningful questions . Unfortunately , the discriminant functions employed by layered ...
While there is only a scanty mathematical understanding of these machines , two complementary viewpoints are discussed which aid formulation of meaningful questions . Unfortunately , the discriminant functions employed by layered ...
Sivu 77
The error - correction training procedures discussed in Sec . 4 : 3 stem from a variety of sources . The fixed - increment and absolute correction rules were first proposed by Rosenblatt , 13 although Widrow and Hoff introduced a ...
The error - correction training procedures discussed in Sec . 4 : 3 stem from a variety of sources . The fixed - increment and absolute correction rules were first proposed by Rosenblatt , 13 although Widrow and Hoff introduced a ...
Sivu 118
The conditions ( if any ) under which this procedure terminates in a solution , when a solution exists , have not yet been determined . 7.3 A disadvantage of the error - correction training methods Throughout this book we have discussed ...
The conditions ( if any ) under which this procedure terminates in a solution , when a solution exists , have not yet been determined . 7.3 A disadvantage of the error - correction training methods Throughout this book we have discussed ...
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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 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 |