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A training algorithm for optimal margin classifiers
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Fast training of support vector machines using sequential minimal optimization
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Newton's Method for Large Bound-Constrained Optimization Problems
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RCV1: A New Benchmark Collection for Text Categorization Research
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Decomposition methods for linear support vector machines
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Sequential conditional Generalized Iterative Scaling
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A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
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Trust region Newton methods for large-scale logistic regression
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Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines
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Ensembles of One Class Support Vector Machines
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CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Dependency-based semantic role labeling of PropBank
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Structured prediction by joint kernel support estimation
Machine Learning
Personalized reading support for second-language web documents by collective intelligence
Proceedings of the 15th international conference on Intelligent user interfaces
Learning Nondeterministic Classifiers
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Bundle Methods for Regularized Risk Minimization
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Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models
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The role of memory in superiority violation gradience
CMCL '10 Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics
Adapting decision DAGs for multipartite ranking
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Document assignment in multi-site search engines
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A coordinate gradient descent method for l1-regularized convex minimization
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Laplacian Support Vector Machines Trained in the Primal
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Text mining for efficient search and assisted creation of clinical trials
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Graphical feature selection for multilabel classification tasks
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ASCOT: assisting search and creation of clinical trials
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Learning to distribute queries into web search nodes
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Ranked tag recommendation systems based on logistic regression
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Supervised learning linear priority dispatch rules for job-shop scheduling
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Multilabel classifiers with a probabilistic thresholding strategy
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Unifying local and global agreement and disagreement classification in online debates
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Learning in probabilistic graphs exploiting language-constrained patterns
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Large-scale logistic regression arises in many applications such as document classification and natural language processing. In this paper, we apply a trust region Newton method to maximize the log-likelihood of the logistic regression model. The proposed method uses only approximate Newton steps in the beginning, but achieves fast convergence in the end. Experiments show that it is faster than the commonly used quasi Newton approach for logistic regression. We also extend the proposed method to large-scale L2-loss linear support vector machines (SVM).