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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 /

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 invite...

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Corporate Authors: ECML PKDD (Conference) Prague, Czech Republic)
Other Authors: ECML PKDD (Conference), Blockeel, Hendrik,, SpringerLink (Online service)
Format: eBook
Language: English
Published: Heidelberg : Springer, 2013.
Physical Description: 1 online resource (xliv, 693 pages) : illustrations.
Series: Lecture notes in computer science.
Lecture notes in computer science. Lecture notes in artificial intelligence ; 8189.
LNCS sublibrary. Artificial intelligence.
Subjects:
Machine learning -- Congresses.
Data mining -- Congresses.
Data mining.
Artificial intelligence.
Table of Contents:
  • Social Network Analysis.
  • Incremental Local Evolutionary Outlier Detection for Dynamic Social Networks /
  • Tengfei Ji, Dongqing Yang, Jun Gao
  • How Long Will She Call Me? Distribution, Social Theory and Duration Prediction /
  • Yuxiao Dong ... et al.
  • Discovering Nested Communities /
  • Nikolaj Tatti, Aristides Gionis
  • CSI: Community-Level Social Influence Analysis /
  • Yasir Mehmood ... et al.
  • Natural Language Processing and Information Extraction.
  • Supervised Learning of Syntactic Contexts for Uncovering Definitions and Extracting Hypernym Relations in Text Databases /
  • Guido Boella, Luigi Di Caro
  • Error Prediction with Partial Feedback /
  • William Darling ... et al.
  • Boot-Strapping Language Identifiers for Short Colloquial Postings /
  • Moises Goldszmidt, Marc Najork, Stelios Paparizos.
  • Ranking and Recommender Systems.
  • A Pairwise Label Ranking Method with Imprecise Scores and Partial Predictions /
  • Sebastien Destercke
  • Learning Socially Optimal Information Systems from Egoistic Users /
  • Karthik Raman, Thorsten Joachims
  • Socially Enabled Preference Learning from Implicit Feedback Data /
  • Julien Delporte ... et al.
  • Cross-Domain Recommendation via Cluster-Level Latent Factor Model /
  • Sheng Gao ... et al.
  • Minimal Shrinkage for Noisy Data Recovery Using Schatten-p Norm Objective /
  • Deguang Kong, Miao Zhang, Chris Ding
  • Matrix and Tensor Analysis.
  • Noisy Matrix Completion Using Alternating Minimization /
  • Suriya Gunasekar ... et al.
  • A Nearly Unbiased Matrix Completion Approach /
  • Dehua Liu ... et al.
  • A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank /
  • Hongyang Zhang, Zhouchen Lin, Chao Zhang.
  • Efficient Rank-one Residue Approximation Method for Graph Regularized Non-negative Matrix Factorization /
  • Qing Liao, Qian Zhang
  • Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data /
  • Kleanthis-Nikolaos Kontonasios, Jilles Vreeken, Tijl De Bie
  • An Analysis of Tensor Models for Learning on Structured Data /
  • Maximilian Nickel, Volker Tresp
  • Learning Modewise Independent Components from Tensor Data Using Multilinear Mixing Model /
  • Haiping Lu
  • Structured Output Prediction, Multi-label and Multi-task Learning.
  • Taxonomic Prediction with Tree-Structured Covariances /
  • Matthew B. Blaschko, Wojciech Zaremba, Arthur Gretton
  • Position Preserving Multi-Output Prediction /
  • Zubin Abraham ... et al.
  • Structured Output Learning with Candidate Labels for Local Parts /
  • Chengtao Li, Jianwen Zhang, Zheng Chen
  • Shared Structure Learning for Multiple Tasks with Multiple Views /
  • Xin Jin ... et al.
  • Using Both Latent and Supervised Shared Topics for Multitask Learning /
  • Ayan Acharya ... et al.
  • Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions /
  • Rodrigo C. Barros ... et al.
  • Multi-core Structural SVM Training /
  • Kai-Wei Chang, Vivek Srikumar, Dan Roth
  • Multi-label Classification with Output Kernels /
  • Yuhong Guo, Dale Schuurmans
  • Transfer Learning.
  • Boosting for Unsupervised Domain Adaptation /
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban
  • Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines /
  • Haitham Bou Ammar ... et al.
  • Bayesian Learning.
  • A Layered Dirichlet Process for Hierarchical Segmentation of Sequential Grouped Data /
  • Adway Mitra, Ranganath B.N., Indrajit Bhattacharya
  • A Bayesian Classifier for Learning from Tensorial Data /
  • Wei Liu ... et al.
  • Prediction with Model-Based Neutrality /
  • Kazuto Fukuchi, Jun Sakuma, Toshihiro Kamishima.
  • Decision-Theoretic Sparsification for Gaussian Process Preference Learning /
  • M. Ehsan Abbasnejad, Edwin V. Bonilla, Scott Sanner
  • Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures /
  • Konstantinos Bousmalis ... et al.
  • Sparsity in Bayesian Blind Source Separation and Deconvolution /
  • Václav Šmídl, Ondřej Tichý
  • Nested Hierarchical Dirichlet Process for Nonparametric Entity-Topic Analysis /
  • Priyanka Agrawal, Lavanya Sita Tekumalla, Indrajit Bhattacharya
  • Graphical Models.
  • Knowledge Intensive Learning: Combining Qualitative Constraints with Causal Independence for Parameter Learning in Probabilistic Models /
  • Shuo Yang, Sriraam Natarajan
  • Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation /
  • Song Liu ... et al.
  • Greedy Part-Wise Learning of Sum-Product Networks /
  • Robert Peharz, Bernhard C. Geiger, Franz Pernkopf
  • From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering /
  • Ramnath Balasubramanyan, Bhavana Dalvi, William W. Cohen.

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