Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 13
Sivu 20
... X2 , . . . , Xr are linearly separable if and only if linear discriminant functions 91 ,
92 , . . . , OR exist such that g : ( X ) > gi ... of X into two subsets Xi and X2 is a
linear dichotomy if and only if a linear discriminant function g exists such that g ( x
) ...
... X2 , . . . , Xr are linearly separable if and only if linear discriminant functions 91 ,
92 , . . . , OR exist such that g : ( X ) > gi ... of X into two subsets Xi and X2 is a
linear dichotomy if and only if a linear discriminant function g exists such that g ( x
) ...
Sivu 84
Other than the fact that a bound on the number of steps exists , thus proving the
theorem , the bound itself is not very useful in estimating how many steps will be
required in a given situation , since it depends on knowledge of a solution vector
...
Other than the fact that a bound on the number of steps exists , thus proving the
theorem , the bound itself is not very useful in estimating how many steps will be
required in a given situation , since it depends on knowledge of a solution vector
...
Sivu 97
There do not yet exist , however , efficient adjustment rules for such thorough
training of a layered machine . In the next three sections , we ... That is , no vector
W exists such that Y . W > 0 for each Y in Yı and ( 6 . 1 ) Y . W < 0 for each Y in Yz
...
There do not yet exist , however , efficient adjustment rules for such thorough
training of a layered machine . In the next three sections , we ... That is , no vector
W exists such that Y . W > 0 for each Y in Yı and ( 6 . 1 ) Y . W < 0 for each Y in Yz
...
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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 |