The nature of statistical learning theory
The nature of statistical learning theory
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Solving large scale linear prediction problems using stochastic gradient descent algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Trust Region Newton Method for Logistic Regression
The Journal of Machine Learning Research
Application of smoothing technique on twin support vector machines
Pattern Recognition Letters
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines
The Journal of Machine Learning Research
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
The Journal of Machine Learning Research
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Pegasos: primal estimated sub-gradient solver for SVM
Mathematical Programming: Series A and B - Special Issue on "Optimization and Machine learning"; Alexandre d’Aspremont • Francis Bach • Inderjit S. Dhillon • Bin Yu
Improvements on Twin Support Vector Machines
IEEE Transactions on Neural Networks
Robust twin support vector machine for pattern classification
Pattern Recognition
Twin support vector machine with Universum data
Neural Networks
Structural twin support vector machine for classification
Knowledge-Based Systems
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Twin support vector machines (TWSVMs), as the representative nonparallel hyperplane classifiers, have shown the effectiveness over standard SVMs from some aspects. However, they still have some serious defects restricting their further study and real applications: (1) They have to compute and store the inverse matrices before training, it is intractable for many applications where data appear with a huge number of instances as well as features; (2) TWSVMs lost the sparseness by using a quadratic loss function making the proximal hyperplane close enough to the class itself. This paper proposes a Sparse Linear Nonparallel Support Vector Machine, termed as L"1-NPSVM, to deal with large-scale data based on an efficient solver-dual coordinate descent (DCD) method. Both theoretical analysis and experiments indicate that our method is not only suitable for large scale problems, but also performs as good as TWSVMs and SVMs.