A new result in the theory and computation of the least-norm solution of a linear program
Journal of Optimization Theory and Applications
The nature of statistical learning theory
The nature of statistical learning theory
Parallel Gradient Distribution in Unconstrained Optimization
SIAM Journal on Control and Optimization
Matrix computations (3rd ed.)
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Lagrangian support vector machines
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Minimization of SC1 functions and the Maratos effect
Operations Research Letters
Learning a complex metabolomic dataset using random forests and support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature space perspectives for learning the kernel
Machine Learning
The Interplay of Optimization and Machine Learning Research
The Journal of Machine Learning Research
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
The Journal of Machine Learning Research
Direct convex relaxations of sparse SVM
Proceedings of the 24th international conference on Machine learning
Towards Effective Visual Data Mining with Cooperative Approaches
Visual Data Mining
Liknon Feature Selection for Microarrays
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Chunking for massive nonlinear kernel classification
Optimization Methods & Software
RV-SVM: An Efficient Method for Learning Ranking SVM
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Analytic center of spherical shells and its application to analytic center machine
Computational Optimization and Applications
Binarized Support Vector Machines
INFORMS Journal on Computing
On the sparseness of 1-norm support vector machines
Neural Networks
The Journal of Machine Learning Research
A smoothing function for 1-norm support vector machines
ICNC'09 Proceedings of the 5th international conference on Natural computation
Feature selection for SVM via optimization of kernel polarization with Gaussian ARD kernels
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
Sparse ensembles using weighted combination methods based on linear programming
Pattern Recognition
The Journal of Machine Learning Research
Learning sparse features on-line for image classification
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Soft computing decision support for a steel sheet incremental cold shaping process
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Evaluating feature selection for SVMs in high dimensions
ECML'06 Proceedings of the 17th European conference on Machine Learning
Dimension reduction vs. variable selection
PARA'04 Proceedings of the 7th international conference on Applied Parallel Computing: state of the Art in Scientific Computing
Integrated classifier hyperplane placement and feature selection
Expert Systems with Applications: An International Journal
1-Norm least squares twin support vector machines
Neurocomputing
An efficient method for learning nonlinear ranking SVM functions
Information Sciences: an International Journal
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Support Vector Machines with L1 penalty for detecting gene-gene interactions
International Journal of Data Mining and Bioinformatics
A minimax probabilistic approach to feature transformation for multi-class data
Applied Soft Computing
Sparse high-dimensional fractional-norm support vector machine via DC programming
Computational Statistics & Data Analysis
A fast algorithm for kernel 1-norm support vector machines
Knowledge-Based Systems
Fuzzy rough based regularization in Generalized Multiple Kernel Learning
Computers & Mathematics with Applications
Feature selection filter for classification of power system operating states
Computers & Mathematics with Applications
| Hi-index | 0.00 |
A fast Newton method, that suppresses input space features, is proposed for a linear programming formulation of support vector machine classifiers. The proposed stand-alone method can handle classification problems in very high dimensional spaces, such as 28,032 dimensions, and generates a classifier that depends on very few input features, such as 7 out of the original 28,032. The method can also handle problems with a large number of data points and requires no specialized linear programming packages but merely a linear equation solver. For nonlinear kernel classifiers, the method utilizes a minimal number of kernel functions in the classifier that it generates.