Open Access
Statistical learning theory
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TLDR
Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.Abstract:
A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.read more
Citations
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Journal ArticleDOI
Learning deep generative models
TL;DR: The aim of the thesis is to demonstrate that deep generative models that contain many layers of latent variables and millions of parameters can be learned efficiently, and that the learned high-level feature representations can be successfully applied in a wide spectrum of application domains, including visual object recognition, information retrieval, and classification and regression tasks.
Journal ArticleDOI
NMR and pattern recognition methods in metabolomics: from data acquisition to biomarker discovery: a review.
Agnieszka Smolinska,Lionel Blanchet,Lionel Blanchet,Lutgarde M. C. Buydens,Sybren S. Wijmenga +4 more
TL;DR: The developments in data acquisition and multivariate analysis of NMR-based metabolomics data are described, with particular emphasis on the metabolomics of Cerebrospinal Fluid and biomarker discovery in Multiple Sclerosis.
Book
PAC-BAYESIAN SUPERVISED CLASSIFICATION: The Thermodynamics of Statistical Learning
TL;DR: An alternative selection scheme based on relative bounds between estimators is described and study, and a two step localization technique which can handle the selection of a parametric model from a family of those is presented.
Journal ArticleDOI
Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images
Ali Tabesh,Mikhail Teverovskiy,Ho-Yuen Pang,V.P. Kumar,David Verbel,Angeliki Kotsianti,Olivier Saidi +6 more
TL;DR: The performance of Gaussian, -nearest neighbor, and support vector machine classifiers together with the sequential forward feature selection algorithm is compared to aggregate color, texture, and morphometric cues at the global and histological object levels for classification.
Proceedings Article
Cherrypick: adaptively unearthing the best cloud configurations for big data analytics
Omid Alipourfard,Hongqiang Harry Liu,Jianshu Chen,Shivaram Venkataraman,Minlan Yu,Ming Zhang +5 more
TL;DR: CherryPick is a system that leverages Bayesian Optimization to build performance models for various applications, and the models are just accurate enough to distinguish the best or close-to-the-best configuration from the rest with only a few test runs.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?