Machine learning and knowledge discovery in databases. European conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012, proceedings / Peter A. Flach, Tijl De Bie, Nello Cristianini (eds.). Part I :
This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were c...
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| Format: | Ebook |
| Language: | English |
| Published: |
Berlin ; New York :
Springer,
[2012]
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| Series: | Lecture notes in computer science. Lecture notes in artificial intelligence ;
7523. LNCS sublibrary. Artificial intelligence. |
| Subjects: | |
| Online Access: | Springer eBooks |
Table of Contents:
- Machine Learning for Robotics / Pieter Abbeel
- Declarative Modeling for Machine Learning and Data Mining / Luc De Raedt
- Machine Learning Methods for Music Discovery and Recommendation / Douglas Eck
- Solving Problems with Visual Analytics: Challenges and Applications / Daniel Keim
- Analyzing Text and Social Network Data with Probabilistic Models / Padhraic Smyth
- Discovering Descriptive Tile Trees by Mining Optimal Geometric Subtiles / Nikolaj Tatti and Jilles Vreeken
- Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees / Matteo Riondato and Eli Upfal
- Smoothing Categorical Data / Arno Siebes and René Kersten
- An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning / Maxime Gasse, Alex Aussem and Haytham Elghazel
- Bayesian Network Classifiers with Reduced Precision Parameters / Sebastian Tschiatschek, Peter Reinprecht, Manfred Mücke and Franz Pernkopf
- Combining Subjective Probabilities and Data in Training Markov Logic Networks / Tivadar Pápai, Shalini Ghosh and Henry Kautz
- Score-Based Bayesian Skill Learning / Shengbo Guo, Scott Sanner, Thore Graepel and Wray Buntine
- A Note on Extending Generalization Bounds for Binary Large-Margin Classifiers to Multiple Classes / Ürün Dogan, Tobias Glasmachers and Christian Igel
- Extension of the Rocchio Classification Method to Multi-modal Categorization of Documents in Social Media / Amin Mantrach and Jean-Michel Renders
- Label-Noise Robust Logistic Regression and Its Applications / Jakramate Bootkrajang and Ata Kabán
- Sentiment Classification with Supervised Sequence Embedding / Dmitriy Bespalov, Yanjun Qi, Bing Bai and Ali Shokoufandeh
- The Bitvector Machine: A Fast and Robust Machine Learning Algorithm for Non-linear Problems / Stefan Edelkamp and Martin Stommel
- Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods / Francis Maes, Pierre Geurts and Louis Wehenkel
- Hypergraph Spectra for Semi-supervised Feature Selection / Zhihong Zhang, Edwin R. Hancock and Xiao Bai
- Learning Neighborhoods for Metric Learning / Jun Wang, Adam Woznica and Alexandros Kalousis
- Massively Parallel Feature Selection: An Approach Based on Variance Preservation / Zheng Zhao, James Cox, David Duling and Warren Sarle
- PCA, Eigenvector Localization and Clustering for Side-Channel Attacks on Cryptographic Hardware Devices / Dimitrios Mavroeidis, Lejla Batina, Twan van Laarhoven and Elena Marchiori
- Classifying Stem Cell Differentiation Images by Information Distance / Xianglilan Zhang, Hongnan Wang, Tony J. Collins, Zhigang Luo and Ming Li
- Distance Metric Learning Revisited / Qiong Cao, Yiming Ying and Peng Li
- Geodesic Analysis on the Gaussian RKHS Hypersphere / Nicolas Courty, Thomas Burger and Pierre-François Marteau
- Boosting Nearest Neighbors for the Efficient Estimation of Posteriors / Roberto D'Ambrosio, Richard Nock, Wafa Bel Haj Ali, Frank Nielsen and Michel Barlaud
- Diversity Regularized Ensemble Pruning / Nan Li, Yang Yu and Zhi-Hua Zhou
- Ensembles on Random Patches / Gilles Louppe and Pierre Geurts
- An Efficiently Computable Support Measure for Frequent Subgraph Pattern Mining / Yuyi Wang and Jan Ramon
- Efficient Graph Kernels by Randomization / Marion Neumann, Novi Patricia, Roman Garnett and Kristian Kersting
- Graph Mining for Object Tracking in Videos / Fabien Diot, Elisa Fromont, Baptiste Jeudy, Emmanuel Marilly and Olivier Martinot
- Hypergraph Learning with Hyperedge Expansion / Li Pu and Boi Faltings
- Nearly Exact Mining of Frequent Trees in Large Networks / Ashraf M. Kibriya and Jan Ramon
- Reachability Analysis and Modeling of Dynamic Event Networks / Kathy Macropol and Ambuj Singh
- CC-MR
- Finding Connected Components in Huge Graphs with MapReduce / Thomas Seidl, Brigitte Boden and Sergej Fries
- Fast Near Neighbor Search in High-Dimensional Binary Data / Anshumali Shrivastava and Ping Li
- Fully Sparse Topic Models / Khoat Than and Tu Bao Ho
- Learning Compact Class Codes for Fast Inference in Large Multi Class Classification / M. Cissé, T. Artières and Patrick Gallinari
- ParCube: Sparse Parallelizable Tensor Decompositions / Evangelos E. Papalexakis, Christos Faloutsos and Nicholas D. Sidiropoulos
- Stochastic Coordinate Descent Methods for Regularized Smooth and Nonsmooth Losses / Qing Tao, Kang Kong, Dejun Chu and Gaowei Wu
- Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets / Haoruo Peng, Zhengyu Wang, Edward Y. Chang, Shuchang Zhou and Zhihua Zhang
- Author Name Disambiguation Using a New Categorical Distribution Similarity / Shaohua Li, Gao Cong and Chunyan Miao
- Lifted Online Training of Relational Models with Stochastic Gradient Methods / Babak Ahmadi, Kristian Kersting and Sriraam Natarajan
- Scalable Relation Prediction Exploiting Both Intrarelational Correlation and Contextual Information / Xueyan Jiang, Volker Tresp, Yi Huang, Maximilian Nickel and Hans-Peter Kriegel
- Relational Differential Prediction / Houssam Nassif, Vítor Santos Costa, Elizabeth S. Burnside and David Page
- Efficient Training of Graph-Regularized Multitask SVMs / Christian Widmer, Marius Kloft, Nico Görnitz and Gunnar Rätsch
- Geometry Preserving Multi-task Metric Learning / Peipei Yang, Kaizhu Huang and Cheng-Lin Liu
- Learning and Inference in Probabilistic Classifier Chains with Beam Search / Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon and Charles Elkan
- Learning Multiple Tasks with Boosted Decision Trees / Jean Baptiste Faddoul, Boris Chidlovskii, Rémi Gilleron and Fabien Torre
- Multi-Task Boosting by Exploiting Task Relationships / Yu Zhang and Dit-Yan Yeung
- Sparse Gaussian Processes for Multi-task Learning / Yuyang Wang and Roni Khardon
- Collective Information Extraction with Context-Specific Consistencies / Peter Kluegl, Martin Toepfer, Florian Lemmerich, Andreas Hotho and Frank Puppe
- Supervised Learning of Semantic Relatedness / Ran El-Yaniv and David Yanay
- Unsupervised Bayesian Part of Speech Inference with Particle Gibbs / Gregory Dubbin and Phil Blunsom
- WikiSent: Weakly Supervised Sentiment Analysis through Extractive Summarization with Wikipedia / Subhabrata Mukherjee and Pushpak Bhattacharyya
- Adaptive Two-View Online Learning for Math Topic Classification / Tam T. Nguyen, Kuiyu Chang and Siu Cheung Hui
- BDUOL: Double Updating Online Learning on a Fixed Budget / Peilin Zhao and Steven C.H. Hoi
- Handling Time Changing Data with Adaptive Very Fast Decision Rules / Petr Kosina and João Gama
- Improved Counter Based Algorithms for Frequent Pairs Mining in Transactional Data Streams / Konstantin Kutzkov
- Mirror Descent for Metric Learning: A Unified Approach / Gautam Kunapuli and Jude Shavlik.