Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 54
Sivu 69
4.3 TLU training procedures Suppose that we have a linear dichotomy of y with two subsets Y. and Y2 and that for some pattern Y in y , a TLU with a weight vector W has a response which is either erroneous ( Y. W < 0 ) or undefined ( Y ...
4.3 TLU training procedures Suppose that we have a linear dichotomy of y with two subsets Y. and Y2 and that for some pattern Y in y , a TLU with a weight vector W has a response which is either erroneous ( Y. W < 0 ) or undefined ( Y ...
Sivu 75
4.5 An error - correction training procedure for R > 2 gi ( X ) A linear machine for classifying patterns belonging ... Each discriminant function can be represented as the dot product of a weight vector with an augmented pattern vector ...
4.5 An error - correction training procedure for R > 2 gi ( X ) A linear machine for classifying patterns belonging ... Each discriminant function can be represented as the dot product of a weight vector with an augmented pattern vector ...
Sivu 103
are adjusted as shown since they are the closest to the Y , pattern hyperplane ( they make the two least - negative dot products with Y ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern ...
are adjusted as shown since they are the closest to the Y , pattern hyperplane ( they make the two least - negative dot products with Y ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern ...
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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 mean vector measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector patterns belonging 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 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 |