34 resultados para Supervector kernel


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Nonmonotonic Logics such as Autoepistemic Logic, Reflective Logic, and Default Logic, are usually defined in terms of set-theoretic fixed-point equations defined over deductively closed sets of sentences of First Order Logic. Such systems may also be represented as necessary equivalences in a Modal Logic stronger than S5 with the added advantage that such representations may be generalized to allow quantified variables crossing modal scopes resulting in a Quantified Autoepistemic Logic, a Quantified Autoepistemic Kernel, a Quantified Reflective Logic, and a Quantified Default Logic. Quantifiers in all these generalizations obey all the normal laws of logic including both the Barcan formula and its converse. Herein, we address the problem of solving some necessary equivalences containing universal quantifiers over modal scopes. Solutions obtained by these methods are then compared to related results obtained in the literature by Circumscription in Second Order Logic since the disjunction of all the solutions of a necessary equivalence containing just normal defaults in these Quantified Logics, is equivalent to that system.

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Mathematics Subject Classification: Primary 35R10, Secondary 44A15

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2000 Mathematics Subject Classification: Primary 46F12, Secondary 44A15, 44A35

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2000 Mathematics Subject Classification: 45A05, 45B05, 45E05,45P05, 46E30

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Mathematics Subject Classification 2010: 35M10, 35R11, 26A33, 33C05, 33E12, 33C20.

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MSC 2010: 44A20, 33C60, 44A10, 26A33, 33C20, 85A99

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MSC 2010: 45DB05, 45E05, 78A45

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AMS subject classification: Primary 34A60, Secondary 49K24.

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AMS subject classification: 49N35,49N55,65Lxx.

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2000 Mathematics Subject Classification: 60K15, 60K20, 60G20,60J75, 60J80, 60J85, 60-08, 90B15.

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2000 Mathematics Subject Classification: 65C05

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2000 Mathematics Subject Classification: 62H30, 62J20, 62P12, 68T99

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This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.

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MSC 2010: 54C35, 54C60.

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2000 Mathematics Subject Classification: 45F15, 45G10, 46B38.