001451190 000__ 05808cam\a2200685\i\4500
001451190 001__ 1451190
001451190 003__ OCoLC
001451190 005__ 20230310004647.0
001451190 006__ m\\\\\o\\d\\\\\\\\
001451190 007__ cr\cn\nnnunnun
001451190 008__ 221115s2022\\\\sz\\\\\\o\\\\\101\0\eng\d
001451190 019__ $$a1350632142$$a1350685966
001451190 020__ $$a9783031188404$$q(electronic bk.)
001451190 020__ $$a3031188403$$q(electronic bk.)
001451190 020__ $$z9783031188398
001451190 020__ $$z303118839X
001451190 0247_ $$a10.1007/978-3-031-18840-4$$2doi
001451190 035__ $$aSP(OCoLC)1350793060
001451190 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dGW5XE$$dYDX$$dEBLCP$$dOCLCF$$dOCLCQ$$dUKAHL
001451190 049__ $$aISEA
001451190 050_4 $$aQ174$$b.I5625 2022eb
001451190 08204 $$a501$$223/eng/20221115
001451190 1112_ $$aInternational Conference on Discovery Science$$n(25th :$$d2022 :$$cMonpellier, France)
001451190 24510 $$aDiscovery science :$$b25th International Conference, DS 2022, Montpellier, France, October 10-12, 2022, proceedings /$$cPoncelet Pascal, Dino Ienco (eds.).
001451190 2463_ $$aDS 2022
001451190 264_1 $$aCham :$$bSpringer,$$c2022.
001451190 300__ $$a1 online resource.
001451190 336__ $$atext$$btxt$$2rdacontent
001451190 337__ $$acomputer$$bc$$2rdamedia
001451190 338__ $$aonline resource$$bcr$$2rdacarrier
001451190 4901_ $$aLecture notes in artificial intelligence
001451190 4901_ $$aLecture notes in computer science ;$$v13601
001451190 4901_ $$aLNCS sublibrary, SL 7, Artificial intelligence
001451190 500__ $$aSelected conference papers.
001451190 500__ $$aIncludes author index.
001451190 5050_ $$aIntro -- Preface -- Organization -- Keynote Talks -- Unsupervised Model Selection in Outlier Detection: The Elephant in the Room -- Coloring Social Relationships -- 35 Years of 'Scientific Discovery: Computational Explorations of the Creative Processes' - From the Early Days to the State of the Art -- Contents -- Regression and Limited Data -- Model Optimization in Imbalanced Regression -- 1 Introduction -- 2 Related Work -- 3 Imbalanced Regression -- 3.1 Relevance Function -- 3.2 Squared Error Relevance Area (SERA) -- 4 Optimization Loss Function for Imbalanced Regression
001451190 5058_ $$a5 Experimental Study -- 5.1 Experimental Setup -- 5.2 Results on Model Optimization -- 5.3 Results in Out-of-Sample -- 6 Conclusions -- A SERA numerical approximation -- B Tables of Results -- References -- Discovery of Differential Equations Using Probabilistic Grammars -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Algebraic Equations and Numeric Differentiation -- 3.2 Differential Equations and Direct Simulation -- 3.3 Parallel Computation -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusion -- References
001451190 5058_ $$aHyperparameter Importance of Quantum Neural Networks Across Small Datasets -- 1 Introduction -- 2 Background -- 2.1 Functional ANOVA -- 2.2 Supervised Learning with Parameterized Quantum Circuits -- 3 Methods -- 3.1 Hyperparameters and Configuration Space -- 3.2 Assessing Hyperparameter Importance -- 3.3 Verifying Hyperparameter Importance -- 4 Dataset and Inclusion Criteria -- 5 Results -- 5.1 Performance Distributions per Dataset -- 5.2 Surrogate Verification -- 5.3 Marginal Contributions -- 5.4 Random Search Verification -- 6 Conclusion -- References
001451190 5058_ $$aImitAL: Learned Active Learning Strategy on Synthetic Data -- 1 Introduction -- 2 Simulating AL on Synthetic Training Data -- 3 Training a Neural Network by Imitation Learning -- 3.1 Imitation Learning -- 3.2 Neural Network Input and Output Encoding -- 3.3 Pre-selection -- 4 Evaluation -- 4.1 Experiment Details -- 4.2 Comparison with Other Active Learning Strategies -- 5 Conclusion -- References -- Incremental/Continual Learning -- Predicting Potential Real-Time Donations in YouTube Live Streaming Services via Continuous-Time Dynamic Graph -- 1 Introduction -- 2 Related Work
001451190 5058_ $$a2.1 Online Live Streaming Service -- 2.2 Dynamic Graph Learning -- 3 Methodology -- 3.1 Dataset -- 3.2 Dynamic Graph Generation -- 3.3 Temporal Graph Neural Network -- 3.4 Strategies for Data Imbalance -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Experiment Setup -- 4.3 Baselines -- 4.4 Evaluation -- 4.5 Case Study -- 5 Conclusion -- References -- Semi-supervised Change Point Detection Using Active Learning -- 1 Introduction -- 2 AL-CPD -- 2.1 Algorithm Outline -- 2.2 Selecting Candidate Change Points -- 2.3 Finding New Candidate Change Points -- 3 Experiments -- 3.1 Datasets
001451190 506__ $$aAccess limited to authorized users.
001451190 520__ $$aThis book constitutes the proceedings of the 25th International Conference on Discovery Science, DS 2022, which took place virtually during October 10-12, 2022. The 27 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 59 submissions. .
001451190 588__ $$aDescription based on print version record.
001451190 650_0 $$aScience$$xPhilosophy$$vCongresses.
001451190 650_0 $$aDiscoveries in science$$vCongresses.
001451190 650_0 $$aResearch$$xData processing$$vCongresses.
001451190 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001451190 655_0 $$aElectronic books.
001451190 7001_ $$aPascal, Poncelet,$$eeditor.
001451190 7001_ $$aIenco, Dino,$$eeditor.
001451190 77608 $$iPrint version:$$aInternational Conference on Discovery Science (25th : 2022 : Online), creator.$$tDiscovery science.$$dCham : Springer Nature Switzerland, 2022$$z9783031188398$$w(OCoLC)1346926468
001451190 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence.
001451190 830_0 $$aLecture notes in computer science ;$$v13601.
001451190 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence.
001451190 852__ $$bebk
001451190 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-18840-4$$zOnline Access$$91397441.1
001451190 909CO $$ooai:library.usi.edu:1451190$$pGLOBAL_SET
001451190 980__ $$aBIB
001451190 980__ $$aEBOOK
001451190 982__ $$aEbook
001451190 983__ $$aOnline
001451190 994__ $$a92$$bISE