Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 4
1 : 3 The problem of what to measure In assuming that the data to be classified
consist of d real numbers , we are obliged to mention , at least briefly , the
difficulties that attend selecting these numbers from any given physical situation .
Before ...
1 : 3 The problem of what to measure In assuming that the data to be classified
consist of d real numbers , we are obliged to mention , at least briefly , the
difficulties that attend selecting these numbers from any given physical situation .
Before ...
Sivu 12
contains an excellent formulation of the pattern - classification problem and also
points out that many schemes currently attracting the attention of engineers have
antecedents in the statistical literature . The problem of data classification has ...
contains an excellent formulation of the pattern - classification problem and also
points out that many schemes currently attracting the attention of engineers have
antecedents in the statistical literature . The problem of data classification has ...
Sivu 89
2 is accomplished by reformulating the R - category problem as a dichotomy
problem in a higher - dimensional space and then applying Theorem 5 . 1 . The
first step is to generate a new set Z of higher - dimensional vectors from the
training ...
2 is accomplished by reformulating the R - category problem as a dichotomy
problem in a higher - dimensional space and then applying Theorem 5 . 1 . The
first step is to generate a new set Z of higher - dimensional vectors from the
training ...
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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 |