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Machine Learning, Optimization, and Big Data de Panos Pardalos

Machine Learning, Optimization, and Big Data

Editat de Panos Pardalos, Mario Pavone, Giovanni Maria Farinella, Vincenzo Cutello
en Limba Engleză Paperback – 6 ian 2016
This bookconstitutes revised selected papers from the First International Workshop onMachine Learning, Optimization, and Big Data, MOD 2015, held in Taormina, Sicily,Italy, in July 2015.
The 32papers presented in this volume were carefully reviewed and selected from 73submissions. They deal with the algorithms, methods and theories relevant indata science, optimization and machine learning. 
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Specificații

ISBN-13: 9783319279251
ISBN-10: 3319279254
Pagini: 388
Ilustrații: XV, 372 p. 115 illus.
Dimensiuni: 155 x 235 x 21 mm
Greutate: 0.59 kg
Ediția:1st edition 2015
Editura: Springer
Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

Learning with discrete least squares on multivariate polynomial spaces using
evaluations at random or low-discrepancy point sets.- Automatic Tuning of Algorithms through Sensitivity Minimization.- Step down and step up statistical procedures for stock selection with Sharp ratio.- Differentiating the multipoint Expected Improvement for optimal batch design.- Dynamic Detection of Transportation Modes using KeypointPrediction.- Effect of the dynamic topology on the performance of PSO-2S algorithm for continuous optimization.- Heuristic for Site-Dependent Truck and Trailer Routing Problem with Soft and Hard Time Windows and Split Deliveries.- Cross-Domain Matrix Factorization for Multiple Implicit-Feedback Domains.- Advanced Metamodeling Techniques Applied to Multidimensional Applications with Piecewise Responses.- Alternating direction method of multipliers for regularized multiclass support vector machines.- Tree-Based Response Surface Analysis.- A Single-Facility Manifold Location Routing Problem with an Application to Supply Chain Management and Robotics.- An Ecient Many-Core Implementation for Semi-Supervised Support Vector Machines.- Intent Recognition in a Simulated Maritime Multi-Agent Domain.- An Adaptive Classification Framework for Unsupervised Model Updating in Nonstationary Environments.- Global Optimization with Sparse and Local Gaussian Process Models.- Condense Mixed Convexity and Optimization with an Application in Data Service Optimization.- SoC-based pattern recognition device for Non Destructive Testing.