Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 9
Sivu 25
... threshold 2.61% 1.30% 0.80% Sub-Saharan Africa Latin America Euro Area 6.8% 3.6% 2.0% 46 92 150 R - B - P R - B - P R - B - P 34% -14% -52% 57% -11% -32% 76% - 6% - 18% tries, based on their group average, as reported in the ILOSTAT ...
... threshold 2.61% 1.30% 0.80% Sub-Saharan Africa Latin America Euro Area 6.8% 3.6% 2.0% 46 92 150 R - B - P R - B - P R - B - P 34% -14% -52% 57% -11% -32% 76% - 6% - 18% tries, based on their group average, as reported in the ILOSTAT ...
Sivu 12
... thresholds located?' and 'Do threshold concepts always propel us forward, outward, and towards an identifiable threshold?' should be revisited together. In this exploration of 'failure as a native informant', I have come to see a threshold ...
... thresholds located?' and 'Do threshold concepts always propel us forward, outward, and towards an identifiable threshold?' should be revisited together. In this exploration of 'failure as a native informant', I have come to see a threshold ...
Sivu 250
... threshold concepts and proposing strategies to support doctoral candidates. Innovations in Education and Teaching International, 46(3), 293–304. Land, R., Rattray, J., & Vivian, P. (2014). Learning in the liminal space: A semiotic ...
... threshold concepts and proposing strategies to support doctoral candidates. Innovations in Education and Teaching International, 46(3), 293–304. Land, R., Rattray, J., & Vivian, P. (2014). Learning in the liminal space: A semiotic ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |