The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Kernel projection algorithm for large-scale SVM problems
Journal of Computer Science and Technology
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
A generalized S-K algorithm for learning v-SVM classifiers
Pattern Recognition Letters
A New Fuzzy Support Vector Machine Based on the Weighted Margin
Neural Processing Letters
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Neural Computation
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Posterior probability support vector Machines for unbalanced data
IEEE Transactions on Neural Networks
On the Equivalence of the SMO and MDM Algorithms for SVM Training
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Simple Clipping Algorithms for Reduced Convex Hull SVM Training
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
A novel geometric approach to binary classification based on scaled convex hulls
IEEE Transactions on Neural Networks
A ν-twin support vector machine (ν-TSVM) classifier and its geometric algorithms
Information Sciences: an International Journal
Least squares twin support vector hypersphere (LS-TSVH) for pattern recognition
Expert Systems with Applications: An International Journal
The robust and efficient adaptive normal direction support vector regression
Expert Systems with Applications: An International Journal
Maximal-margin approach for cost-sensitive learning based on scaled convex hull
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
A fast algorithm for kernel 1-norm support vector machines
Knowledge-Based Systems
| Hi-index | 0.01 |
Based on the geometric interpretation of support vector machines (SVMs), this paper presents a general technique that allows almost all the existing L"2-norm penalty based geometric algorithms, including Gilbert's algorithm, Schlesinger-Kozinec's (SK) algorithm and Mitchell-Dem'yanov-Malozemov's (MDM) algorithm, to be softened to achieve the corresponding learning L"1-SVM classifiers. Intrinsically, the resulting soft algorithms are to find @e-optimal nearest points between two soft convex hulls. Theoretical analysis has indicated that our proposed soft algorithms are essentially generalizations of the corresponding existing hard algorithms, and consequently, they have the same properties of convergence and almost the identical cost of computation. As a specific example, the problem of solving @n-SVMs by the proposed soft MDM algorithm is investigated and the corresponding solution procedure is specified and analyzed. To validate the general soft technique, several real classification experiments are conducted with the proposed L"1-norm based MDM algorithms and numerical results have demonstrated that their performance is competitive to that of the corresponding L"2-norm based algorithms, such as SK and MDM algorithms.