Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Contours of equal probability density ( 21 ? – 20122122 + 2z2 = constant ) are ellipses , centered on the origin , whose major axes lie along the line 21 = 22. The eccentricities of the ellipses are equal to 2012 Vi + 1012 а When 012 is ...
Contours of equal probability density ( 21 ? – 20122122 + 2z2 = constant ) are ellipses , centered on the origin , whose major axes lie along the line 21 = 22. The eccentricities of the ellipses are equal to 2012 Vi + 1012 а When 012 is ...
Sivu 58
Since ( E ) ; has rank equal to rank QiQi ' , which is equal to rank Qi , and since rank Qi < min ( d , N :) , rank ( 2 ) ... If Ni Zd , Qi will have rank equal to d if and only if there are no linear dependencies among the rows of Qi .
Since ( E ) ; has rank equal to rank QiQi ' , which is equal to rank Qi , and since rank Qi < min ( d , N :) , rank ( 2 ) ... If Ni Zd , Qi will have rank equal to d if and only if there are no linear dependencies among the rows of Qi .
Sivu 89
Y = = > 2. We denote each of the R - 1 vectors in Z generated by Y by the symbol Zil ; ( Y ) , j = 1 , ... , R , ; * i . j 3. Let the ith block of D components of each Zil ; ( Y ) be set equal to Y for j 1 , 4.
Y = = > 2. We denote each of the R - 1 vectors in Z generated by Y by the symbol Zil ; ( Y ) , j = 1 , ... , R , ; * i . j 3. Let the ith block of D components of each Zil ; ( Y ) be set equal to Y for j 1 , 4.
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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 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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |