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Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II

  • Conference proceedings
  • © 2013

Overview

Editors:
  1. Hendrik Blockeel

    1. Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium

    You can also search for this editor inPubMed Google Scholar

  2. Kristian Kersting

    1. Fraunhofer IAIS, Department of Knowledge Discovery, Schloss Birlinghoven, University of Bonn, Sankt Augustin, Germany

    You can also search for this editor inPubMed Google Scholar

  3. Siegfried Nijssen

    1. LIACS, Universiteit Leiden, Leiden, The Netherlands

    You can also search for this editor inPubMed Google Scholar

  4. Filip Železný

    1. Department of Computer Science and Engineering, Czech Technical University, Prague 6, Czech Republic

    You can also search for this editor inPubMed Google Scholar

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Part of the book series:Lecture Notes in Computer Science (LNCS, volume 8189)

Part of the book sub series:Lecture Notes in Artificial Intelligence (LNAI)

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Conference proceedings info: ECML PKDD 2013.

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About this book

This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.

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Table of contents (43 papers)

  2. Social Network Analysis

    2. How Long Will She Call Me? Distribution, Social Theory and Duration Prediction

      • Yuxiao Dong, Jie Tang, Tiancheng Lou, Bin Wu, Nitesh V. Chawla
      Pages 16-31

    3. Discovering Nested Communities

      • Nikolaj Tatti, Aristides Gionis
      Pages 32-47

    4. CSI: Community-Level Social Influence Analysis

      • Yasir Mehmood, Nicola Barbieri, Francesco Bonchi, Antti Ukkonen
      Pages 48-63

  3. Natural Language Processing and Information Extraction

    2. Error Prediction with Partial Feedback

      • William Darling, Cédric Archambeau, Shachar Mirkin, Guillaume Bouchard
      Pages 80-94

    3. Boot-Strapping Language Identifiers for Short Colloquial Postings

      • Moises Goldszmidt, Marc Najork, Stelios Paparizos
      Pages 95-111

  4. Ranking and Recommender Systems

    2. Learning Socially Optimal Information Systems from Egoistic Users

      • Karthik Raman, Thorsten Joachims
      Pages 128-144

    3. Socially Enabled Preference Learning from Implicit Feedback Data

      • Julien Delporte, Alexandros Karatzoglou, Tomasz Matuszczyk, Stéphane Canu
      Pages 145-160

    4. Cross-Domain Recommendation via Cluster-Level Latent Factor Model

      • Sheng Gao, Hao Luo, Da Chen, Shantao Li, Patrick Gallinari, Jun Guo
      Pages 161-176

    5. Minimal Shrinkage for Noisy Data Recovery Using Schatten-p Norm Objective

      • Deguang Kong, Miao Zhang, Chris Ding
      Pages 177-193

  5. Matrix and Tensor Analysis

    1. Noisy Matrix Completion Using Alternating Minimization

      • Suriya Gunasekar, Ayan Acharya, Neeraj Gaur, Joydeep Ghosh
      Pages 194-209

    2. A Nearly Unbiased Matrix Completion Approach

      • Dehua Liu, Tengfei Zhou, Hui Qian, Congfu Xu, Zhihua Zhang
      Pages 210-225

    5. Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data

      • Kleanthis-Nikolaos Kontonasios, Jilles Vreeken, Tijl De Bie
      Pages 256-271

    6. An Analysis of Tensor Models for Learning on Structured Data

      • Maximilian Nickel, Volker Tresp
      Pages 272-287

Editors and Affiliations

  • Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium

    Hendrik Blockeel

  • Fraunhofer IAIS, Department of Knowledge Discovery, Schloss Birlinghoven, University of Bonn, Sankt Augustin, Germany

    Kristian Kersting

  • LIACS, Universiteit Leiden, Leiden, The Netherlands

    Siegfried Nijssen

  • Department of Computer Science and Engineering, Czech Technical University, Prague 6, Czech Republic

    Filip Železný

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Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook JPY 5719
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Other ways to access

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