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| |  Online-Ressource |
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| Titel: | Learning and Intelligent Optimization |
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| Titelzusatz: | 14th International Conference, LION 14, Athens, Greece, May 24–28, 2020, Revised Selected Papers |
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| Mitwirkende: | Kotsireas, Ilias S. [HerausgeberIn]  |
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| | Pardalos, Panos M. [HerausgeberIn] |
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| Verf.angabe: | edited by Ilias S. Kotsireas, Panos M. Pardalos |
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| Ausgabe: | 1st ed. 2020. |
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| Verlagsort: | Cham |
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| | Cham |
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| Verlag: | Springer International Publishing |
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| | Imprint: Springer |
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| E-Jahr: | 2020 |
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| Jahr: | 2020. |
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| | 2020. |
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| Umfang: | 1 Online-Ressource(XIV, 430 p. 91 illus., 62 illus. in color.) |
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| Gesamttitel/Reihe: | Theoretical Computer Science and General Issues ; 12096 |
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| ISBN: | 978-3-030-53552-0 |
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| Abstract: | Optimization for Urban Air Mobility -- A Matheuristic Algorithm for solving the Vehicle Routing Problem with Cross-Docking -- Physical activity as a risk factor in the progression of Osteoarthritis: A machine learning perspective -- QPTAS for the CVRP with a moderate number of routes in a metric space of any xed doubling dimension -- Early detection of eating disorders through machine learning techniques -- On Finding Minimum Cardinality Subset of Vectors with a Constraint on the Sum of Squared Euclidean Pairwise Distances -- Practical Approximation Algorithms for Stabbing Special Families of Line Segments with Equal Disks -- A Class of Linear Programs Solvable by Coordinate-wise Minimization -- Travel times equilibration procedure for route- flow traffic assignment problem -- confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms -- Randomized Algorithms for Some Sequence Clustering Problems -- Hyper-parameterized Dialectic Search for Non-Linear Box-constrained Optimization with Heterogenous Variable Types -- Least Squares K-SVCR Multi-class Classification -- Active learning based framework for image captioning corpus creation -- Reducing space search in combinatorial optimization using machine learning tools -- Impact of the Discretization of VOCs for Cancer Prediction using a Multi-Objective Algorithm -- AUGMECON2 method for a bi-objective U-shaped assembly line balancing problem -- Two-Channel Conflict-Free Square Grid Aggregation -- Online Stacking using RL with Positional and Tactical Features -- Power consumption estimation in data centers using machine learning techniques -- Automated tuning of a column generation algorithm -- Pool-based Realtime Algorithm Configuration: A Preselection Bandit Approach -- A Memetic Approach for the Unicost Set Covering Problem -- Dynamic Visual Few-Shot Learning through Parameter Prediction Network -- An Alternating DCA-based Approach for Reduced-Rank Multitask Linear Regression with Covariance Estimation -- PSO-based cooperative learning using chunking -- The problem of the hospital surgery department debottlenecking -- Learning Optimal Control of Water Distribution Networks through Sequential Model-based Optimization -- Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization -- A Pareto Simulated Annealing for the Integrated Problem of Berth and Quay Crane Scheduling at Maritime Container Terminals with Multiple Objectives and Stochastic Arrival Times of Vessels -- HotelSimu: Simulation-based Optimization for Hotel Dynamic Pricing -- Heuristic Search Strategies for Noisy Optimization -- Uncertainty of efficient frontier in portfolio optimization -- Learning to configure mathematical programming solvers by mathematical programming -- Convex Hulls in Solving Multiclass Pattern Recognition Problem -- Least Correntropic Loss Regression -- Novelty Discovery with Kernel Minimum Enclosing Balls -- DESICOM as Metaheuristic Search. |
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| | This book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 14, held in Athens, Greece, in May 2020. The 37 full papers presented together with one invited paper have been carefully reviewed and selected from 75 submissions. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components. Due to the COVID-19 pandemic, LION 14 was not held as a physical meeting. . |
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| DOI: | doi:10.1007/978-3-030-53552-0 |
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| URL: | Resolving-System: https://doi.org/10.1007/978-3-030-53552-0 |
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| | DOI: https://doi.org/10.1007/978-3-030-53552-0 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Bibliogr. Hinweis: | Erscheint auch als : Druck-Ausgabe |
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| | Erscheint auch als : Druck-Ausgabe |
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| K10plus-PPN: | 1726031632 |
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| Lokale URL UB: |  Zum Volltext |
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978-3-030-53552-0
Learning and Intelligent Optimization / Kotsireas, Ilias S. [HerausgeberIn]; 2020. (Online-Ressource)
68622941