Machine Learning and Knowledge Discovery in Databases

1. Front Matter

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2. Ensembles

   1. AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy

      • Baidya Nath Saha, Gautam Kunapuli, Nilanjan Ray, Joseph A. Maldjian, Sriraam Natarajan

      Pages 1-16

   2. Parallel Boosting with Momentum

      • Indraneel Mukherjee, Kevin Canini, Rafael Frongillo, Yoram Singer

      Pages 17-32

   3. Inner Ensembles: Using Ensemble Methods Inside the Learning Algorithm

      • Houman Abbasian, Chris Drummond, Nathalie Japkowicz, Stan Matwin

      Pages 33-48

3. Statistical Learning

   1. Learning Discriminative Sufficient Statistics Score Space for Classification

      • Xiong Li, Bin Wang, Yuncai Liu, Tai Sing Lee

      Pages 49-64

   2. The Stochastic Gradient Descent for the Primal L1-SVM Optimization Revisited

      • Constantinos Panagiotakopoulos, Petroula Tsampouka

      Pages 65-80

   3. Bundle CDN: A Highly Parallelized Approach for Large-Scale ℓ1-Regularized Logistic Regression

      • Yatao Bian, Xiong Li, Mingqi Cao, Yuncai Liu

      Pages 81-95

   4. MORD: Multi-class Classifier for Ordinal Regression

      • Kostiantyn Antoniuk, Vojtěch Franc, Václav Hlaváč

      Pages 96-111

   5. Identifiability of Model Properties in Over-Parameterized Model Classes

      • Manfred Jaeger

      Pages 112-127

4. Semi-supervised Learning

   1. Exploratory Learning

      • Bhavana Dalvi, William W. Cohen, Jamie Callan

      Pages 128-143

   2. Semi-supervised Gaussian Process Ordinal Regression

      • P. K. Srijith, Shirish Shevade, S. Sundararajan

      Pages 144-159

   3. Influence of Graph Construction on Semi-supervised Learning

      • Celso André R. de Sousa, Solange O. Rezende, Gustavo E. A. P. A. Batista

      Pages 160-175

   4. Tractable Semi-supervised Learning of Complex Structured Prediction Models

      • Kai-Wei Chang, S. Sundararajan, S. Sathiya Keerthi

      Pages 176-191

   5. PSSDL: Probabilistic Semi-supervised Dictionary Learning

      • Behnam Babagholami-Mohamadabadi, Ali Zarghami, Mohammadreza Zolfaghari, Mahdieh Soleymani Baghshah

      Pages 192-207

5. Unsupervised Learning

   1. Embedding with Autoencoder Regularization

      • Wenchao Yu, Guangxiang Zeng, Ping Luo, Fuzhen Zhuang, Qing He, Zhongzhi Shi

      Pages 208-223

   2. Reduced-Rank Local Distance Metric Learning

      • Yinjie Huang, Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos

      Pages 224-239

   3. Learning Exemplar-Represented Manifolds in Latent Space for Classification

      • Shu Kong, Donghui Wang

      Pages 240-255

   4. Locally Linear Landmarks for Large-Scale Manifold Learning

      • Max Vladymyrov, Miguel Á. Carreira-Perpiñán

      Pages 256-271

6. Subgroup Discovery, Outlier Detection and Anomaly Detection

   1. Discovering Skylines of Subgroup Sets

      • Matthijs van Leeuwen, Antti Ukkonen

      Pages 272-287

   2. Difference-Based Estimates for Generalization-Aware Subgroup Discovery

      • Florian Lemmerich, Martin Becker, Frank Puppe

      Pages 288-303

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.

• bayesian network
• data mining
• graph-based methods
• parallel optimization
• social responsibility

• 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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