Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 29
To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , ... . . , f m are functions of the Xi , i = 1 , d . The first d components of F are x1 ?, x2 * , xd ?; the next dd – 1 ...
To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , ... . . , f m are functions of the Xi , i = 1 , d . The first d components of F are x1 ?, x2 * , xd ?; the next dd – 1 ...
Sivu 50
ties might then be : number of typical patterns belonging to category 1 for which the ith component equals one Pi Ni number of ... 3.5 , we assumed that the pattern components were statistically independent , binary , random variables .
ties might then be : number of typical patterns belonging to category 1 for which the ith component equals one Pi Ni number of ... 3.5 , we assumed that the pattern components were statistically independent , binary , random variables .
Sivu 111
Since U is a binary vector with P components there are 2P distinct U vectors . For some of these U vectors , H ( U ) = 1 , and for the remaining , H ( U ) = -1 . Let Hy be a matrix ) ) whose rows consist of those U vectors for which H ...
Since U is a binary vector with P components there are 2P distinct U vectors . For some of these U vectors , H ( U ) = 1 , and for the remaining , H ( U ) = -1 . Let Hy be a matrix ) ) whose rows consist of those U vectors for which H ...
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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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |