Titlebook: Machine Learning, Optimization, and Big Data; First International Panos Pardalos,Mario Pavone,Vincenzo Cutello Conference proceedings 2015

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Global Optimization with Sparse and Local Gaussian Process Models,based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected

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Condense Mixed Convexity and Optimization with an Application in Data Service Optimization,ming is widely used in data based optimization research that uses matrix theory (see for example [.]). Important elements of matrix theory, such as Hessian matrices, are well studied for continuous (see for example [.]) and discrete [.] functions, however matrix theory for functions with mixed (i.e.

Bravura

SoC-Based Pattern Recognition Systems for Non Destructive Testing, reliability of distribution chains. We present an optimized implementation of common pattern recognition algorithms that performs NDT on factory products. To the aim of enhancing the industrial integration, our implementation is highly optimized to work on SoC-based (System on Chip: an integrated c

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concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these m

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Giovanni Migliorati concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these m

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Piero Conca,Giovanni Stracquadanio,Giuseppe Nicosia concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these m

Brochure

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Sébastien Marmin,Clément Chevalier,David Ginsbourgerce that can be readily adapted to other models.Reviews many .This book explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the specification of a likelihood function. As a result, ABC can be used to estimate posterior distributio