Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 36
Sivu 19
S 13 Rus Sja S 34 R3 3 RA S , ( redundant ) 12 S 23 R 2 24 FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions S 13 R ; 1 S 12 RE Rg $ 23 FIGURE 2.3 Decision regions for a minimum - distance ...
S 13 Rus Sja S 34 R3 3 RA S , ( redundant ) 12 S 23 R 2 24 FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions S 13 R ; 1 S 12 RE Rg $ 23 FIGURE 2.3 Decision regions for a minimum - distance ...
Sivu 96
The TLUs in the second and subsequent layers have as their inputs the outputs of the TLUS Response X * Pattern FIGURE 6.1 A network of TLUS TLUs in the preceding layer only . The output of the single TLU in the final layer is the ...
The TLUs in the second and subsequent layers have as their inputs the outputs of the TLUS Response X * Pattern FIGURE 6.1 A network of TLUS TLUs in the preceding layer only . The output of the single TLU in the final layer is the ...
Sivu 106
In this figure the points marked represent patterns belonging to X1 , and the points marked o represent patterns belonging to X2 . Clearly the TLUs in the first layer of the desired layered machine must at least implement hyperplanes ...
In this figure the points marked represent patterns belonging to X1 , and the points marked o represent patterns belonging to X2 . Clearly the TLUs in the first layer of the desired layered machine must at least implement hyperplanes ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
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 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 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 specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |