Brefeld, Ulf - Fakult�t IV Elektrotechnik und Informatik, Technische Universit�t Berlin

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Brefeld, Ulf - Fakult�t IV Elektrotechnik und Informatik, Technische Universit�t Berlin

Co-EM Support Vector Learning Ulf Brefeld brefeld@informatik.hu-berlin.de
Supervised Clustering of Streaming Data for Email Batch Detection Peter Haider haider@mpi-inf.mpg.de
Subgradient Methods for Maximum Margin Structured Learning Nathan D. Ratliff ndr@ri.cmu.edu
Non-sparse Multiple Kernel Learning Marius Kloft
Semi-supervised Clustering using Combinatorial MRFs Ron Bekkerman ronb@cs.umass.edu
Support Vector Machines with Example Dependent Costs
Efficient and Accurate p-Norm Multiple Kernel Learning
Non-Sparse Regularization and Efficient Training with Multiple Kernels
Feature Selection for Density Level-Sets Marius Kloft1
Document Assignment in Multi-site Search Engines Ulf Brefeld
A Supplementary Material Before we prove Theorem 2, let us first show a useful result which justifies switching from Tikhonov
Technical Report on Information-Based Induction Sciences 2009 (IBIS2009)
Efficient Classification of Images with Taxonomies
Active and Semi-supervised Data Domain Description
Exact and Approximate Inference for Annotating Graphs with Structural SVMs
Semi-supervised Structured Prediction Models DISSERTATION
Cost-based Ranking in Input Output Spaces Ulf Brefeld
Efficient Co-Regularised Least Squares Regression Ulf Brefeld brefeld@informatik.hu-berlin.de
Multi-View Discriminative Sequential Learning Ulf Brefeld, Christoph Buscher, and Tobias Scheffer
Perceptron and SVM Learning with Generalized Cost Models
Learning Linear Classifiers Sensitive to Example Dependent and Noisy Costs
EDY: an Algorithm for Discovering Complex Events in Symbolic Ugo Galassi galassi@mfn.unipmn.it
MAP estimation in MRFs via rank aggregation Rahul Gupta
Sequential Models for Sentiment Prediction Yi Mao ymao@ecn.purdue.edu
Active Learning with Perceptron for Structured Output Dan Roth danr@uiuc.edu
Journal of Machine Learning Research 11 (2010) 555-580 Submitted 12/08; Revised 9/09; Published 2/10 Approximate Tree Kernels
AUC Maximizing Support Vector Learning Ulf Brefeld brefeld@informatik.hu-berlin.de
Discriminative Identification of Duplicates Peter Haider, Ulf Brefeld, and Tobias Scheffer
Automatic Feature Selection for Anomaly Detection Marius Kloft
Semi-Supervised Learning for Structured Output Variables Ulf Brefeld brefeld@informatik.hu-berlin.de
Active Learning for Network Intrusion Detection Nico Grnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld
Transductive Support Vector Machines for Structured Variables Alexander Zien alexander.zien@tuebingen.mpg.de
A Support Vector Machine Classifier for Gene Name Recognition
Multi-View Hidden Markov Perceptrons Ulf Brefeld and Christoph Buscher and Tobias Scheffer
Learning from Partially Annotated Sequences Eraldo R. Fernandes