| Description |
1 online resource (688 pages) |
| Series |
Communications in computer and information science, 1865-0937 ; v. 1167 |
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Communications in computer and information science ; v. 1167.
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| Contents |
Intro -- Preface -- Organization -- Contents -- Part I -- Contents -- Part II -- Automating Data Science -- The ABC of Data: A Classifying Framework for Data Readiness -- 1 Introduction -- 2 The Framework -- 2.1 Data Bands -- 2.2 Quality Scores -- 3 The Different Levels of Data Readiness -- 3.1 Band C: Conceive -- 3.2 Band B: Believe -- 3.3 Band A: Analyze -- 3.4 Band AA: Allow Analysis -- 3.5 Band AAA: A Clean Dataset -- 4 Deployment -- 5 Discussion -- References -- Automating Common Data Science Matrix Transformations -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Method |
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5 Experiments -- 5.1 Results with Artificial Data -- 5.2 Results with Real Examples -- 6 Conclusions and Future Work -- References -- DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets -- 1 Introduction -- 2 Automatic Statisticians via Deep Probabilistic Models -- 2.1 Deep and Tractable Probabilistic Models -- 2.2 Mixed Sum-Product Networks (MSPNs) -- 3 DeepNotebooks -- Constructing Data Reports in the Form of Python Notebooks Based on MSPNs -- 4 Computing Statistical Measures Using MSPNs -- 5 Illustrations of DeepNotebooks -- 6 Conclusions -- References |
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HyperUCB: Hyperparameter Optimization Using Contextual Bandits -- 1 Introduction -- 2 Background -- 2.1 Problem Setting -- 2.2 Hyperband -- 2.3 Contextual Bandits -- 3 Contextual HyperUCB -- 4 Empirical Study -- 5 Conclusion and Future Work -- References -- Learning Parsers for Technical Drawings -- 1 Introduction -- 2 Identifying Technical Drawing Elements -- 3 Inductive Logic Programs for Parsing -- 3.1 Standard ILP -- 3.2 ILP with Bootstrapping -- 4 Experiments -- 4.1 Learning Set-Up -- 4.2 Results -- References -- Meta-learning of Textual Representations -- 1 Introduction -- 2 Related Work |
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3 Recommending Textual Representations -- 4 Experiments and Results -- 5 Conclusion and Future Work -- References -- ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optimization Embedded Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Towards ReinBo -- 3.2 Connections to Hyperband -- 3.3 Connection and Extension to Hierarchical Reinforcement Learning -- 3.4 Procedures of ReinBo -- 4 Experiments -- 4.1 Implementation, Comparison Methods and Setups -- 4.2 Experiment Results -- 5 Summary and Future Work -- References |
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Supervised Human-Guided Data Exploration -- 1 Introduction and Related Work -- 2 Background -- 3 Supervised Exploration -- 4 Experimental Evaluation -- 4.1 Scalability -- 4.2 Stability -- 4.3 Supervised Exploration of german Data -- 4.4 Supervised Exploration of Bnc Data -- 4.5 Identification of Churners -- 5 Conclusions -- References -- SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract) -- 1 Introduction -- 2 Introduction to SynthLog -- 2.1 ProbLog by Example -- 2.2 SynthLog Theories -- 2.3 A Language for Data Science -- 3 Case Study: Auto-Completion -- 4 Conclusion |
| Summary |
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Machine learning -- Congresses
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Data mining -- Congresses
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Data mining
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Machine learning
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| Genre/Form |
Electronic books
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proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Cellier, Peggy.
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Driessens, Kurt
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| ISBN |
9783030438234 |
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3030438236 |
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