Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 15
A Unified Concept Kumar S. Ray. Chapter. 2. Pattern. Classification. Based. on. Conventional. Interpretation. of. MFI. Abstract Our aim is to design a pattern classifier using fuzzy relational calculus (FRC) which was initially proposed by ...
A Unified Concept Kumar S. Ray. Chapter. 2. Pattern. Classification. Based. on. Conventional. Interpretation. of. MFI. Abstract Our aim is to design a pattern classifier using fuzzy relational calculus (FRC) which was initially proposed by ...
Sivu 28
... classifier that uses them rises to 77.19% Often, bagging produces a combined model that outperforms the model that is built using a ... Pattern Classification Using Ensemble Methods 2.4 The Boosting Algorithm 2.5 The AdaBoost Algorithm.
... classifier that uses them rises to 77.19% Often, bagging produces a combined model that outperforms the model that is built using a ... Pattern Classification Using Ensemble Methods 2.4 The Boosting Algorithm 2.5 The AdaBoost Algorithm.
Sivu 153
... classifier function that maps between brain patterns and experimental conditions. d The trained classifier function defines a decision boundary (red dashed ... Pattern Classification of Spatiotemporal Association Features in fMRI Data 153.
... classifier function that maps between brain patterns and experimental conditions. d The trained classifier function defines a decision boundary (red dashed ... Pattern Classification of Spatiotemporal Association Features in fMRI Data 153.
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 |