Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 4
Most of the measurement - selection techniques that have been developed are
specific to a particular application and thus would not be appropriate topics in a
general treatment such as this . We shall henceforth assume that the d ...
Most of the measurement - selection techniques that have been developed are
specific to a particular application and thus would not be appropriate topics in a
general treatment such as this . We shall henceforth assume that the d ...
Sivu 30
That is , the parameters which determine a specific quadric function from among
a whole family of quadric functions appear linearly in the function . There is an
important class of function families whose parameters have this property . We
shall ...
That is , the parameters which determine a specific quadric function from among
a whole family of quadric functions appear linearly in the function . There is an
important class of function families whose parameters have this property . We
shall ...
Sivu 45
1 ) , we could calculate Lx ( i ) for any specific X and for all possible values of i ( i
= 1 , . . . , R ) . Suppose for some specific X , Lx ( i ) is a minimum for i = in . That is
, Lx ( io ) < Lx ( 2 ) for i = 1 , . . . , R . We would minimize the conditional average ...
1 ) , we could calculate Lx ( i ) for any specific X and for all possible values of i ( i
= 1 , . . . , R ) . Suppose for some specific X , Lx ( i ) is a minimum for i = in . That is
, Lx ( io ) < Lx ( 2 ) for i = 1 , . . . , R . We would minimize the conditional average ...
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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 Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements 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 specific 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 |