The nature of statistical learning theory
The nature of statistical learning theory
Properties of support vector machines
Neural Computation
On the Complexity of a Practical Interior-Point Method
SIAM Journal on Optimization
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Efficient Computation and Model Selection for the Support Vector Regression
Neural Computation
SVM-based feature extraction for face recognition
Pattern Recognition
An improved algorithm for the solution of the regularization path of support vector machine
IEEE Transactions on Neural Networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Just relax: convex programming methods for identifying sparse signals in noise
IEEE Transactions on Information Theory
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This paper presents a novel algorithm for the initial configuration to the model selection problem in the two-class support vector machine (SVM) classification when fitting the entire path of SVM solutions for every value of the regularization parameter. Instead of using quadratic programming for initialization in the conventional two-class SVM regularization path fitting methods, we propose a piecewise linear method which reduces the computational cost significantly. Furthermore, an efficient treatment is provided to deal with the singular case where the data set contains linearly dependent points, duplicate points or nearly duplicate points. The performance of the proposed algorithm in terms of computational complexity and the ability to handle singular cases are backed by strict mathematical analysis and proof, and verified by the experimental results.