Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 19
Sivu 12
... development of " optical preprocessors " for visual data . 14 REFERENCES 1 Hawkins , J .: Self - organizing Systems : A Review and Commentary , Proc . IKE , vol . 49 , no . 1 , pp . 31-48 , January , 1961 . 2 Sebestyen , G .: " Decision ...
... development of " optical preprocessors " for visual data . 14 REFERENCES 1 Hawkins , J .: Self - organizing Systems : A Review and Commentary , Proc . IKE , vol . 49 , no . 1 , pp . 31-48 , January , 1961 . 2 Sebestyen , G .: " Decision ...
Sivu 13
... U.S. Army Signal Research and Development Laboratory under Contract DA 36-039- SC - 78343 and continuation , 1962 and 1963 . CHAPTER 2 SOME IMPORTANT DISCRIMINANT FUNCTIONS : THEIR PROPERTIES AND TRAINABLE PATTERN CLASSIFIERS 13.
... U.S. Army Signal Research and Development Laboratory under Contract DA 36-039- SC - 78343 and continuation , 1962 and 1963 . CHAPTER 2 SOME IMPORTANT DISCRIMINANT FUNCTIONS : THEIR PROPERTIES AND TRAINABLE PATTERN CLASSIFIERS 13.
Sivu 41
... Development Center Technical Documentary Report RADC - TDR - 64-32 , February , 1964 . 8 : Geometrical and Statistical Properties of Linear Threshold Devices , Stanford Electronics Laboratories Technical Report 6107-1 , May , 1964 . 9 ...
... Development Center Technical Documentary Report RADC - TDR - 64-32 , February , 1964 . 8 : Geometrical and Statistical Properties of Linear Threshold Devices , Stanford Electronics Laboratories Technical Report 6107-1 , May , 1964 . 9 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding 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 discriminant functions linear machine linearly separable 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 space Stanford step subsidiary discriminant 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 |