Machine learning and knowledge discovery in databases : European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings.: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings. Part I (2020)

• Record Type: Book
• Other Titles: ECML PKDD 2019
• Further Information: Note: Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet (eds.).
• Other Names: Brefeld, Ulf; Fromont, Elisa; Hotho, Andreas; Knobbe, Arno J; Maathuis, Marloes, 1978-; Robardet, Céline; ECML PKDD (Conference)
• Contents: Intro -- Preface -- Organization -- Abstracts of Invited Talks -- Programming by Input-Output Examples -- Machine Learning for Robust and Fast Control of Manipulation Under Disturbances -- Palaeontology as a Computational Science -- Data Driven Algorithm Design -- The Quest for the Perfect Image Representation -- Contents -- Part I -- Contents -- Part II -- Contents -- Part III -- Pattern Mining -- DEvIANT: Discovering Significant Exceptional (Dis- )Agreement Within Groups -- 1 Introduction -- 2 Background and Related Work -- 3 Problem Definition 3.1 Intra-group Agreement Measure: Krippendorff's Alpha (A) -- 3.2 Mining Significant Patterns with Krippendorff's Alpha -- 4 Exceptional Contexts: Evaluation and Pruning -- 4.1 Pruning the Search Space -- 5 On Handling Variability of Outcomes Among Raters -- 6 A Branch-and-Bound Solution: Algorithm DEvIANT -- 7 Empirical Evaluation -- 8 Conclusion and Future Directions -- References -- Maximal Closed Set and Half-Space Separations in Finite Closure Systems -- 1 Introduction -- 2 Preliminaries -- 3 Half-Space and Maximal Closed Set Separation -- 3.1 Half-Space Separation 3.2 Maximal Closed Set Separation -- 4 Kakutani Closure Systems -- 4.1 Kakutani Closure Systems over Graphs -- 4.2 Experimental Results -- 5 Non-Kakutani Closure Systems -- 5.1 Experimental Results -- 6 Concluding Remarks -- References -- Sets of Robust Rules, and How to Find Them -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Notation -- 3.2 Minimum Description Length -- 4 Theory -- 4.1 The Problem, Informally -- 4.2 MDL for Rule Sets -- 4.3 The Problem, Formally -- 5 Algorithm -- 6 Experiments -- 7 Discussion -- 8 Conclusion -- References Clustering, Anomaly and Outlier Detection, and Autoencoders -- A Framework for Deep Constrained Clustering -- Algorithms and Advances -- 1 Introduction -- 2 Related Work -- 3 Deep Constrained Clustering Framework -- 3.1 Deep Embedded Clustering -- 3.2 Different Types of Constraints -- 3.3 Preventing Trivial Solution -- 3.4 Extensions to High-Level Domain Knowledge-Based Constraints -- 4 Putting It All Together -- Efficient Training Strategy -- 5 Experiments -- 5.1 Datasets -- 5.2 Implementation Details -- 5.3 Experimental Results -- 6 Conclusion and Future Work -- References A Framework for Parallelizing Hierarchical Clustering Methods -- 1 Introduction -- 2 Preliminaries -- 3 A Framework for Parallelizing Hierarchical Clustering Algorithms -- 4 Fast Parallel Algorithms for Clustering -- 4.1 Distributed Divisive k-Clustering -- 4.2 Distributed Centroid-Linkage -- 4.3 From Bounded Length Dependency Chains to Parallel Algorithms -- 5 Experimental Results -- 6 Conclusion and Future Work -- References -- Unsupervised and Active Learning Using Maximin-Based Anomaly Detection -- 1 Introduction -- 2 Definitions and Problem Specification -- 3 Methodology
• Publisher Details: Cham : Springer
• Publication Date: 2020
• Extent: 1 online resource (798 pages)
• Subjects: 006.3/1; Machine learning -- Congresses; Data mining -- Congresses; Data mining; Machine learning; Electronic books; Electronic books; Conference papers and proceedings
• Languages: English
• ISBNs: 9783030461508; 3030461505
• Related ISBNs: 9783030461492
• Notes: Note: Includes bibliographical references and author index.; Note: Print version record.
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• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.