The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Improved Generalization Through Explicit Optimization of Margins
Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Neural Computation
Sparseness of support vector machines
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Leveraging the margin more carefully
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Empirical risk minimization for support vector classifiers
IEEE Transactions on Neural Networks
The Journal of Machine Learning Research
Sparse probabilistic classifiers
Proceedings of the 24th international conference on Machine learning
On the relation between multi-instance learning and semi-supervised learning
Proceedings of the 24th international conference on Machine learning
Discriminatively regularized least-squares classification
Pattern Recognition
Noisy Image Segmentation by a Robust Clustering Algorithm Based on DC Programming and DCA
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Gene Selection for Cancer Classification Using DCA
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Catenary Support Vector Machines
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Fast semi-supervised SVM classifiers using a priori metric information
Optimization Methods & Software - Mathematical programming in data mining and machine learning
Non-smoothness in classification problems
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART I
Robust support vector regression in the primal
Neural Networks
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Feature Weighting Using Margin and Radius Based Error Bound Optimization in SVMs
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Margin and Radius Based Multiple Kernel Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Maximum margin clustering made practical
IEEE Transactions on Neural Networks
Robust truncated support vector regression
Expert Systems with Applications: An International Journal
Minimum sum-of-squares clustering by DC programming and DCA
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
A semi-supervised approach for reject inference in credit scoring using SVMs
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Large margin learning of Bayesian classifiers based on Gaussian mixture models
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Batch and online learning algorithms for nonconvex neyman-pearson classification
ACM Transactions on Intelligent Systems and Technology (TIST)
Maximal Discrepancy for Support Vector Machines
Neurocomputing
Support Vector Machines with the Ramp Loss and the Hard Margin Loss
Operations Research
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Speedy local search for semi-supervised regularized least-squares
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Can irrelevant data help semi-supervised learning, why and how?
Proceedings of the 20th ACM international conference on Information and knowledge management
A weakly-supervised approach to argumentative zoning of scientific documents
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A DC programming approach for solving the symmetric Eigenvalue Complementarity Problem
Computational Optimization and Applications
Practical collapsed variational bayes inference for hierarchical dirichlet process
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling disease progression via fused sparse group lasso
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust twin support vector machine for pattern classification
Pattern Recognition
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Structured ramp loss minimization for machine translation
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Indexed block coordinate descent for large-scale linear classification with limited memory
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Regularized bundle methods for convex and non-convex risks
The Journal of Machine Learning Research
Robust feature selection for SVMs under uncertain data
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
DCA based algorithms for feature selection in semi-supervised support vector machines
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
New and efficient DCA based algorithms for minimum sum-of-squares clustering
Pattern Recognition
The C-loss function for pattern classification
Pattern Recognition
Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers
Pattern Recognition Letters
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Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis. However, in this work we show how non-convexity can provide scalability advantages over convexity. We show how concave-convex programming can be applied to produce (i) faster SVMs where training errors are no longer support vectors, and (ii) much faster Transductive SVMs.