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Home > Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II > Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II |
Hendrik Blockeel; Kristian Kersting; Siegfried Nijssen; Filip Železný
Springer Berlin Heidelberg
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
Authors

Song Liu; John A. Quinn; Michael U. Gutmann; Masashi Sugiyama

ARI Id

1664696131246_2767002

Access

Not Available Free

Pages

596-611

DOI

10.1007/978-3-642-40991-2_38

Chapter URL

https://rd.springer.com/chapter/10.1007/978-3-642-40991-2_38

Table of Contents of Book
Showing 1 to 20 of 45 entries
Chapters/HeadingsAuthor(s)PagesInfo
Front Matter
Incremental Local Evolutionary Outlier Detection for Dynamic Social Networks
Tengfei Ji; Dongqing Yang; Jun Gao
1-15
How Long Will She Call Me? Distribution, Social Theory and Duration Prediction
Yuxiao Dong; Jie Tang; Tiancheng Lou; Bin Wu; Nitesh V. Chawla
16-31
Discovering Nested Communities
Nikolaj Tatti; Aristides Gionis
32-47
CSI: Community-Level Social Influence Analysis
Yasir Mehmood; Nicola Barbieri; Francesco Bonchi; Antti Ukkonen
48-63
Supervised Learning of Syntactic Contexts for Uncovering Definitions and Extracting Hypernym Relations in Text Databases
Guido Boella; Luigi Di Caro
64-79
Error Prediction with Partial Feedback
William Darling; Cédric Archambeau; Shachar Mirkin; Guillaume Bouchard
80-94
Boot-Strapping Language Identifiers for Short Colloquial Postings
Moises Goldszmidt; Marc Najork; Stelios Paparizos
95-111
A Pairwise Label Ranking Method with Imprecise Scores and Partial Predictions
Sebastien Destercke
112-127
Learning Socially Optimal Information Systems from Egoistic Users
Karthik Raman; Thorsten Joachims
128-144
Socially Enabled Preference Learning from Implicit Feedback Data
Julien Delporte; Alexandros Karatzoglou; Tomasz Matuszczyk; Stéphane Canu
145-160
Cross-Domain Recommendation via Cluster-Level Latent Factor Model
Sheng Gao; Hao Luo; Da Chen; Shantao Li; Patrick Gallinari; Jun Guo
161-176
Minimal Shrinkage for Noisy Data Recovery Using Schatten-p Norm Objective
Deguang Kong; Miao Zhang; Chris Ding
177-193
Noisy Matrix Completion Using Alternating Minimization
Suriya Gunasekar; Ayan Acharya; Neeraj Gaur; Joydeep Ghosh
194-209
A Nearly Unbiased Matrix Completion Approach
Dehua Liu; Tengfei Zhou; Hui Qian; Congfu Xu; Zhihua Zhang
210-225
A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank
Hongyang Zhang; Zhouchen Lin; Chao Zhang
226-241
Efficient Rank-one Residue Approximation Method for Graph Regularized Non-negative Matrix Factorization
Qing Liao; Qian Zhang
242-255
Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data
Kleanthis-Nikolaos Kontonasios; Jilles Vreeken; Tijl De Bie
256-271
An Analysis of Tensor Models for Learning on Structured Data
Maximilian Nickel; Volker Tresp
272-287
Learning Modewise Independent Components from Tensor Data Using Multilinear Mixing Model
Haiping Lu
288-303
Chapters/HeadingsAuthor(s)PagesInfo
Showing 1 to 20 of 45 entries
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