Buch, Englisch, 372 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 587 g
First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers
Buch, Englisch, 372 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 587 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-319-27925-1
Verlag: Springer International Publishing
This book
constitutes revised selected papers from the First International Workshop on
Machine Learning, Optimization, and Big Data, MOD 2015, held in Taormina, Sicily,
Italy, in July 2015.
The 32
papers presented in this volume were carefully reviewed and selected from 73
submissions. They deal with the algorithms, methods and theories relevant in
data science, optimization and machine learning.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Learning with discrete least squares on multivariate polynomial spaces usingevaluations 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.
