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001482699 019__ $$a1406407298
001482699 020__ $$a9783031445057$$q(electronic bk.)
001482699 020__ $$a3031445058$$q(electronic bk.)
001482699 020__ $$z303144504X
001482699 020__ $$z9783031445040
001482699 0247_ $$a10.1007/978-3-031-44505-7$$2doi
001482699 035__ $$aSP(OCoLC)1406411788
001482699 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dEBLCP$$dYDX$$dOCLCO
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001482699 050_4 $$aQ325.5$$b.L56 2023eb
001482699 08204 $$a006.3/1$$223/eng/20231102
001482699 1112_ $$aLION (Conference)$$n(17th :$$d2023 :$$cNice, France)
001482699 24510 $$aLearning and intelligent optimization :$$b17th International Conference, LION 17, Nice, France, June 4-8, 2023, Revised selected papers /$$cMeinolf Sellmann, Kevin Tierney, editors.
001482699 2463_ $$aLION 17
001482699 260__ $$aCham :$$bSpringer,$$c2023.
001482699 300__ $$a1 online resource (628 p.).
001482699 4901_ $$aLecture Notes in Computer Science ;$$v14286
001482699 500__ $$a2 Bayesian Optimization (BO) and Stochastic Kriging (SK): Notation and Terminology
001482699 5050_ $$aIntro -- Preface -- Organization -- Contents -- Anomaly Classification to Enable Self-healing in Cyber Physical Systems Using Process Mining -- 1 Introduction -- 1.1 Process Mining -- 1.2 Anomalies in Event Logs -- 1.3 Ensemble Machine Learning Approaches -- 1.4 Models Used -- 2 Literature Survey -- 3 Problem Statement and Dataset Description -- 4 Methodology -- 4.1 Event Logs and Process Discovery -- 4.2 Conformance Checking -- 4.3 Anomaly Classification -- 5 Results and Discussions -- 5.1 Model Preparation -- 5.2 Dataset Generation -- 5.3 Conformance Checking
001482699 5058_ $$a5.4 Bagging and Boosting Classification -- 6 Conclusion -- References -- Hyper-box Classification Model Using Mathematical Programming -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Statement -- 3.2 Mathematical Formulation -- 3.3 Testing Phase -- 3.4 Illustrative Example -- 4 Computational Results -- 5 Concluding Remarks -- References -- A Leak Localization Algorithm in Water Distribution Networks Using Probabilistic Leak Representation and Optimal Transport Distance -- 1 Introduction -- 1.1 Motivations -- 1.2 Related Works -- 1.3 Our Contributions
001482699 5058_ $$a1.4 Content Organization -- 2 The Wasserstein Distance -- 2.1 Basic Definitions -- 2.2 Wasserstein Barycenter -- 3 Wasserstein Enabled Leak Localization -- 3.1 Generation of Leak Scenarios -- 3.2 Clustering in the Wasserstein Space -- 3.3 Evaluation Metrics -- 4 Experimental Results -- 4.1 Data Resources -- 4.2 Computational Results -- 5 Conclusions, Limitations, and Perspectives -- References -- Fast and Robust Constrained Optimization via Evolutionary and Quadratic Programming -- 1 Introduction and Related Work -- 2 Problem Formulation and Background Material -- 2.1 Particle Swarm Optimization
001482699 5058_ $$a2.2 Sequential Linear Quadratic Programming -- 3 The Proposed UPSO-QP Approach -- 3.1 Local QP Problems -- 3.2 UPSO for Constrained Optimization -- 3.3 Considerations -- 4 Experiments -- 4.1 Numerical Constrained Optimization Problems -- 4.2 Constrained Optimization with Noisy Functions Values -- 4.3 Evaluation on High Dimensional Problems -- 5 Concluding Remarks -- References -- Bayesian Optimization for Function Compositions with Applications to Dynamic Pricing -- 1 Introduction -- 1.1 Related Work -- 1.2 Dynamic Pricing and Learning -- 1.3 Contributions and Organization
001482699 5058_ $$a2 Problem Description -- 2.1 BO for Function Composition -- 2.2 Bayesian Optimization for Dynamic Pricing -- 3 Proposed Method -- 3.1 Statistical Model and GP Regression -- 3.2 cEI and cUCB Acquisition Functions -- 4 Experiments and Results -- 4.1 Results on Test Functions -- 4.2 Results for Demand Pricing Experiments -- 4.3 Runtime Comparisons with State of the Art -- 5 Conclusion -- References -- A Bayesian Optimization Algorithm for Constrained Simulation Optimization Problems with Heteroscedastic Noise -- 1 Introduction
001482699 506__ $$aAccess limited to authorized users.
001482699 520__ $$aThis book constitutes the refereed proceedings of the 17th International Conference on Learning and Intelligent Optimization, LION-17, held in Nice, France, during June 4-8, 2023. The 40 full papers presented have been carefully reviewed and selected from 83 submissions. They focus on all aspects of unleashing the potential of integrating machine learning and optimization approaches, including automatic heuristic selection, intelligent restart strategies, predict-then-optimize, Bayesian optimization, and learning to optimize.
001482699 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 2, 2023).
001482699 650_6 $$aApprentissage automatique$$vCongrès.
001482699 650_0 $$aMachine learning$$vCongresses.$$vCongresses$$0(DLC)sh2008107143
001482699 655_0 $$aElectronic books.
001482699 7001_ $$aSellmann, Meinolf.
001482699 7001_ $$aTierney, Kevin$$c(Computer scientist)$$0(OCoLC)oca00260695
001482699 77608 $$iPrint version:$$aSellmann, Meinolf$$tLearning and Intelligent Optimization$$dCham : Springer International Publishing AG,c2023$$z9783031445040
001482699 830_0 $$aLecture notes in computer science ;$$v14286.
001482699 852__ $$bebk
001482699 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-44505-7$$zOnline Access$$91397441.1
001482699 909CO $$ooai:library.usi.edu:1482699$$pGLOBAL_SET
001482699 980__ $$aBIB
001482699 980__ $$aEBOOK
001482699 982__ $$aEbook
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