Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 6
the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region Ri is the set of points which map into the ith category number . For convenience , we shall arbitrarily assume that patterns which lie ...
the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region Ri is the set of points which map into the ith category number . For convenience , we shall arbitrarily assume that patterns which lie ...
Sivu 19
There are R ( R – 1 ) / 2 such equations and thus the same number of surfaces Sij . For d = 2 , a linear surface is called a line ; for d = 3 , a plane ; and for d > 3 , a hyperplane . Thus , the decision surfaces of a linear machine ...
There are R ( R – 1 ) / 2 such equations and thus the same number of surfaces Sij . For d = 2 , a linear surface is called a line ; for d = 3 , a plane ; and for d > 3 , a hyperplane . Thus , the decision surfaces of a linear machine ...
Sivu 119
If the covariance matrices of the normal densities are equal , then the decision surface which minimizes the probability of error is a hyperplane perpendicular to the line segment joining the means of the two density functions .
If the covariance matrices of the normal densities are equal , then the decision surface which minimizes the probability of error is a hyperplane perpendicular to the line segment joining the means of the two density functions .
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable 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 selected separable shown side 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 |