Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 28
J . When these conditions are met , both the matrix A and the quadratic form are
called positive definite . If A has one or more of its eigenvalues equal to zero and
all the others positive , then the quadratic form will never be negative , and it and
...
J . When these conditions are met , both the matrix A and the quadratic form are
called positive definite . If A has one or more of its eigenvalues equal to zero and
all the others positive , then the quadratic form will never be negative , and it and
...
Sivu 100
If the responses of at least 12 ( Nxl + 1 ) of these negatively responding TLUs
were changed from - 1 to + 1 , then the majority of the committee TLUs would
have positive responses , and the machine would respond correctly to Yk . For
example ...
If the responses of at least 12 ( Nxl + 1 ) of these negatively responding TLUs
were changed from - 1 to + 1 , then the majority of the committee TLUs would
have positive responses , and the machine would respond correctly to Yk . For
example ...
Sivu 101
Thus , if Yk causes a majority of the committee TLUs to respond negatively , we
adjust the Y2 | | Nal + 1 ) weight vectors making the leastnegative ( but not
positive ) dot products with Yk . If the weight vector W ; ( ) is among this set of 2 ( |
1x1 + ...
Thus , if Yk causes a majority of the committee TLUs to respond negatively , we
adjust the Y2 | | Nal + 1 ) weight vectors making the leastnegative ( but not
positive ) dot products with Yk . If the weight vector W ; ( ) is among this set of 2 ( |
1x1 + ...
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding 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 illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable 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
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 |