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ORCID16th PKDD / 23rd ECML 2012: Bristol, UK
- Peter A. Flach
, Tijl De Bie, Nello Cristianini:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I. Lecture Notes in Computer Science 7523, Springer 2012, ISBN 978-3-642-33459-7
Invited Talks
- Pieter Abbeel:
Machine Learning for Robotics. 1 - Luc De Raedt:
Declarative Modeling for Machine Learning and Data Mining. 2-3 - Douglas Eck:
Machine Learning Methods for Music Discovery and Recommendation. 4 - Daniel A. Keim:
Solving Problems with Visual Analytics: Challenges and Applications. 5-6 - Padhraic Smyth:
Analyzing Text and Social Network Data with Probabilistic Models. 7-8
Association Rules and Frequent Patterns
- Nikolaj Tatti, Jilles Vreeken:
Discovering Descriptive Tile Trees - By Mining Optimal Geometric Subtiles. 9-24 - Matteo Riondato, Eli Upfal:
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees. 25-41 - Arno Siebes, René Kersten:
Smoothing Categorical Data. 42-57
Bayesian Learning and Graphical Models
- Maxime Gasse, Alex Aussem, Haytham Elghazel:
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. 58-73 - Sebastian Tschiatschek, Peter Reinprecht, Manfred Mücke, Franz Pernkopf:
Bayesian Network Classifiers with Reduced Precision Parameters. 74-89 - Tivadar Papai, Shalini Ghosh, Henry A. Kautz:
Combining Subjective Probabilities and Data in Training Markov Logic Networks. 90-105 - Shengbo Guo, Scott Sanner, Thore Graepel, Wray L. Buntine:
Score-Based Bayesian Skill Learning. 106-121
Classification
- Ürün Dogan, Tobias Glasmachers, Christian Igel:
A Note on Extending Generalization Bounds for Binary Large-Margin Classifiers to Multiple Classes. 122-129 - Amin Mantrach, Jean-Michel Renders:
Extension of the Rocchio Classification Method to Multi-modal Categorization of Documents in Social Media. 130-142 - Jakramate Bootkrajang, Ata Kabán:
Label-Noise Robust Logistic Regression and Its Applications. 143-158 - Dmitriy Bespalov, Yanjun Qi, Bing Bai, Ali Shokoufandeh:
Sentiment Classification with Supervised Sequence Embedding. 159-174 - Stefan Edelkamp, Martin Stommel:
The Bitvector Machine: A Fast and Robust Machine Learning Algorithm for Non-linear Problems. 175-190
Dimensionality Reduction, Feature Selection and Extraction
- Francis Maes, Pierre Geurts, Louis Wehenkel:
Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods. 191-206 - Zhihong Zhang, Edwin R. Hancock, Xiao Bai:
Hypergraph Spectra for Semi-supervised Feature Selection. 207-222 - Jun Wang, Adam Woznica, Alexandros Kalousis:
Learning Neighborhoods for Metric Learning. 223-236 - Zheng Zhao, James Cox, David Duling, Warren Sarle:
Massively Parallel Feature Selection: An Approach Based on Variance Preservation. 237-252 - Dimitrios Mavroeidis, Lejla Batina, Twan van Laarhoven, Elena Marchiori:
PCA, Eigenvector Localization and Clustering for Side-Channel Attacks on Cryptographic Hardware Devices. 253-268
Distance-Based Methods and Kernels
- Xianglilan Zhang, Hongnan Wang, Tony J. Collins, Zhigang Luo, Ming Li:
Classifying Stem Cell Differentiation Images by Information Distance. 269-282 - Qiong Cao, Yiming Ying, Peng Li:
Distance Metric Learning Revisited. 283-298 - Nicolas Courty, Thomas Burger, Pierre-François Marteau:
Geodesic Analysis on the Gaussian RKHS Hypersphere. 299-313
Ensemble Methods
- Roberto D'Ambrosio, Richard Nock, Wafa Bel Haj Ali, Frank Nielsen, Michel Barlaud:
Boosting Nearest Neighbors for the Efficient Estimation of Posteriors. 314-329 - Nan Li, Yang Yu, Zhi-Hua Zhou:
Diversity Regularized Ensemble Pruning. 330-345 - Gilles Louppe, Pierre Geurts:
Ensembles on Random Patches. 346-361
Graph and Tree Mining
- Yuyi Wang, Jan Ramon:
An Efficiently Computable Support Measure for Frequent Subgraph Pattern Mining. 362-377 - Marion Neumann, Novi Patricia, Roman Garnett, Kristian Kersting:
Efficient Graph Kernels by Randomization. 378-393 - Fabien Diot, Élisa Fromont, Baptiste Jeudy, Emmanuel Marilly, Olivier Martinot:
Graph Mining for Object Tracking in Videos. 394-409 - Li Pu, Boi Faltings:
Hypergraph Learning with Hyperedge Expansion. 410-425 - Ashraf M. Kibriya, Jan Ramon:
Nearly Exact Mining of Frequent Trees in Large Networks. 426-441 - Kathy Macropol, Ambuj K. Singh:
Reachability Analysis and Modeling of Dynamic Event Networks. 442-457
Large-Scale, Distributed and Parallel Mining and Learning
- Thomas Seidl, Brigitte Boden, Sergej Fries:
CC-MR - Finding Connected Components in Huge Graphs with MapReduce. 458-473 - Anshumali Shrivastava, Ping Li:
Fast Near Neighbor Search in High-Dimensional Binary Data. 474-489 - Khoat Than, Tu Bao Ho:
Fully Sparse Topic Models. 490-505 - Moustapha Cissé, Thierry Artières, Patrick Gallinari:
Learning Compact Class Codes for Fast Inference in Large Multi Class Classification. 506-520 - Evangelos E. Papalexakis, Christos Faloutsos, Nicholas D. Sidiropoulos:
ParCube: Sparse Parallelizable Tensor Decompositions. 521-536 - Qing Tao, Kang Kong, Dejun Chu, Gao-wei Wu:
Stochastic Coordinate Descent Methods for Regularized Smooth and Nonsmooth Losses. 537-552 - Haoruo Peng, Zhengyu Wang, Edward Y. Chang, Shuchang Zhou, Zhihua Zhang:
Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets. 553-568
Multi-Relational Mining and Learning
- Shaohua Li, Gao Cong, Chunyan Miao:
Author Name Disambiguation Using a New Categorical Distribution Similarity. 569-584 - Babak Ahmadi, Kristian Kersting, Sriraam Natarajan:
Lifted Online Training of Relational Models with Stochastic Gradient Methods. 585-600 - Xueyan Jiang, Volker Tresp, Yi Huang, Maximilian Nickel, Hans-Peter Kriegel:
Scalable Relation Prediction Exploiting Both Intrarelational Correlation and Contextual Information. 601-616 - Houssam Nassif, Vítor Santos Costa, Elizabeth S. Burnside, David Page:
Relational Differential Prediction. 617-632
Multi-Task Learning
- Christian Widmer, Marius Kloft, Nico Görnitz, Gunnar Rätsch:
Efficient Training of Graph-Regularized Multitask SVMs. 633-647 - Peipei Yang, Kaizhu Huang, Cheng-Lin Liu:
Geometry Preserving Multi-task Metric Learning. 648-664 - Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan:
Learning and Inference in Probabilistic Classifier Chains with Beam Search. 665-680 - Jean Baptiste Faddoul, Boris Chidlovskii, Rémi Gilleron, Fabien Torre:
Learning Multiple Tasks with Boosted Decision Trees. 681-696 - Yu Zhang, Dit-Yan Yeung:
Multi-Task Boosting by Exploiting Task Relationships. 697-710 - Yuyang Wang, Roni Khardon:
Sparse Gaussian Processes for Multi-task Learning. 711-727
Natural Language Processing
- Peter Klügl, Martin Toepfer, Florian Lemmerich, Andreas Hotho, Frank Puppe:
Collective Information Extraction with Context-Specific Consistencies. 728-743 - Ran El-Yaniv, David Yanay:
Supervised Learning of Semantic Relatedness. 744-759 - Gregory Dubbin, Phil Blunsom:
Unsupervised Bayesian Part of Speech Inference with Particle Gibbs. 760-773 - Subhabrata Mukherjee, Pushpak Bhattacharyya:
WikiSent: Weakly Supervised Sentiment Analysis through Extractive Summarization with Wikipedia. 774-793
Online Learning and Data Streams
- Tam T. Nguyen, Kuiyu Chang, Siu Cheung Hui:
Adaptive Two-View Online Learning for Math Topic Classification. 794-809 - Peilin Zhao, Steven C. H. Hoi:
BDUOL: Double Updating Online Learning on a Fixed Budget. 810-826 - Petr Kosina, João Gama:
Handling Time Changing Data with Adaptive Very Fast Decision Rules. 827-842 - Konstantin Kutzkov:
Improved Counter Based Algorithms for Frequent Pairs Mining in Transactional Data Streams. 843-858 - Gautam Kunapuli, Jude W. Shavlik:
Mirror Descent for Metric Learning: A Unified Approach. 859-874
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