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Bounds on the sample complexity of Bayesian learning using information theory
 and the VC dimension,” in Proc. Conf. Comp. Learning Theory
, 1991
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Support Vector Regression with ANOVA Decomposition Kernels
, 1997
"... Support Vector Machines using ANOVA Decomposition Kernels (SVAD) [Vapng] are a way of imposing a structure on multidimensional kernels which are generated as the tensor product of onedimensional kernels. This gives more accurate control over the capacity of the learning machine (VCdimension) . SVA ..."
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Cited by 37 (1 self)
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Support Vector Machines using ANOVA Decomposition Kernels (SVAD) [Vapng] are a way of imposing a structure on multidimensional kernels which are generated as the tensor product of onedimensional kernels. This gives more accurate control over the capacity of the learning machine (VCdimension) . SVAD uses ideas from ANOVA decomposition methods and extends them to generate kernels which directly implement these ideas. SVAD is used with spline kernels and results show that SVAD performs better than the respective non ANOVA decomposition kernel. The Boston housing data set from UCI has been tested on Bagging [Bre94] and Support Vector methods before [DBK + 97] and these results are compared to the SVAD method. 1 Introduction In this paper we will introduce ANOVA kernels for support vector machines. We firstly introduce multiplicative kernels, which form the basis of the ANOVA kernels, then we introduce the general ANOVA decomposition idea. From this we derive ANOVA kernels and lastly sh...
Model Selection in an Ensemble Framework
, 2006
"... We like to present a method to build ensemble models based on an extended crossvalidation approach. The crossvalidation puts several model classes in a tournament and selects the best performing model with respect to the validation set. This leads to a model selection strategy and an estimation of ..."
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Cited by 3 (1 self)
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We like to present a method to build ensemble models based on an extended crossvalidation approach. The crossvalidation puts several model classes in a tournament and selects the best performing model with respect to the validation set. This leads to a model selection strategy and an estimation of the expected modelling error.
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"... In this paper we study a Bayesian or averagecase model of concept learning with a twofold goal: to provide more precise characterizations of learning curve (sample complexity) behavior that depend on properties of both the prior distribution over concepts and the sequence of instances seen by the l ..."
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In this paper we study a Bayesian or averagecase model of concept learning with a twofold goal: to provide more precise characterizations of learning curve (sample complexity) behavior that depend on properties of both the prior distribution over concepts and the sequence of instances seen by the learner, and to smoothly unite in a common framework the popular statistical physics and VC dimension theories of learning curves. To achieve this, we undertake a systematic investigation and comparison of two fundamental quantities in learning and information theory: the probability ofan incorrect prediction for an optimal learning algorithm, and the Shannon information gain. This study leads to a new understanding of the sample complexity of learning in several existing models. 1
doi:10.1155/2008/218097 Research Article Classification Models for Early Detection of Prostate Cancer
, 2008
"... We investigate the performance of different classification models and their ability to recognize prostate cancer in an early stage. We build ensembles of classification models in order to increase the classification performance. We measure the performance of our models in an extensive crossvalidati ..."
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We investigate the performance of different classification models and their ability to recognize prostate cancer in an early stage. We build ensembles of classification models in order to increase the classification performance. We measure the performance of our models in an extensive crossvalidation procedure and compare different classification models. The datasets come from clinical examinations and some of the classification models are already in use to support the urologists in their clinical work. Copyright © 2008 Joerg D. Wichard et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.