Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 36
... d ) of linear dichotomies of X achievable by a hyperplane constrained to
contain all the points of Z . We shall assume that ... of Z are in general position ,
meaning , in this case , that no ( K – 2 ) - dimensional hyperplane contains all of
them .
... d ) of linear dichotomies of X achievable by a hyperplane constrained to
contain all the points of Z . We shall assume that ... of Z are in general position ,
meaning , in this case , that no ( K – 2 ) - dimensional hyperplane contains all of
them .
Sivu 102
The reader could assume , for example , that Yi contains Y , and that Y2 contains
– Y , and - Y3 . ... The shaded regions indicate those regions that must each
contain one of the weight vectors before the process can successfully terminate
in ...
The reader could assume , for example , that Yi contains Y , and that Y2 contains
– Y , and - Y3 . ... The shaded regions indicate those regions that must each
contain one of the weight vectors before the process can successfully terminate
in ...
Sivu 105
Two of these cells contain one pattern each ; one cell contains four patterns ; and
one cell contains two patterns . Each nonempty cell in pattern space corresponds
to a vertex in Ii space . Thus , the four patterns 1 , 4 , 5 , and 8 all map into the ...
Two of these cells contain one pattern each ; one cell contains four patterns ; and
one cell contains two patterns . Each nonempty cell in pattern space corresponds
to a vertex in Ii space . Thus , the four patterns 1 , 4 , 5 , and 8 all map into the ...
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Sisältö
Preface vii | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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
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 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |