Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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... ( or " adaptive ' ) approach is an example of the latter . 2. Properties of various families of discriminant functions used by trainable machines . 3. Training theorems ( including proofs of the perceptron convergence vii.
... ( or " adaptive ' ) approach is an example of the latter . 2. Properties of various families of discriminant functions used by trainable machines . 3. Training theorems ( including proofs of the perceptron convergence vii.
Sivu 11
We examine the properties of some in detail , and present block diagrams to suggest the manner in which they might be employed . Chapter 3 will investigate decision - theoretic parametric training methods .
We examine the properties of some in detail , and present block diagrams to suggest the manner in which they might be employed . Chapter 3 will investigate decision - theoretic parametric training methods .
Sivu 16
Here we shall define several families of discriminant functions and study their properties , leaving the subject of training to later chapters . One of the simplest is the family of linear functions to which we now turn .
Here we shall define several families of discriminant functions and study their properties , leaving the subject of training to later chapters . One of the simplest is the family of linear functions to which we now turn .
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
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 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 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 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 |