Kernel projection algorithm for large-scale SVM problems
Journal of Computer Science and Technology
Optimization over Zonotopes and Training Support Vector Machines
WADS '01 Proceedings of the 7th International Workshop on Algorithms and Data Structures
Convex Hull in Feature Space for Support Vector Machines
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Decomposition methods for linear support vector machines
Neural Computation
A generalized S-K algorithm for learning v-SVM classifiers
Pattern Recognition Letters
Training Support Vector Machines Using Gilbert's Algorithm
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Invariances in kernel methods: From samples to objects
Pattern Recognition Letters
QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines
The Journal of Machine Learning Research
Maximum-Gain Working Set Selection for SVMs
The Journal of Machine Learning Research
A general soft method for learning SVM classifiers with L1-norm penalty
Pattern Recognition
Geometrical synthesis of MLP neural networks
Neurocomputing
The Kernelized Geometrical Bisection Methods
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A Confident Majority Voting Strategy for Parallel and Modular Support Vector Machines
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
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
Cycle-breaking acceleration of SVM training
Neurocomputing
Coresets for polytope distance
Proceedings of the twenty-fifth annual symposium on Computational geometry
AD-SVMs: A light extension of SVMs for multicategory classification
International Journal of Hybrid Intelligent Systems - Data Mining and Hybrid Intelligent Systems
Nearest Neighbor Convex Hull Classification Method for Face Recognition
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Human motion recognition using support vector machines
Computer Vision and Image Understanding
Softdoublemaxminover: perceptron-like training of support vector machines
IEEE Transactions on Neural Networks
A novel geometric approach to binary classification based on scaled convex hulls
IEEE Transactions on Neural Networks
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Proximity queries using separating bounding volumes
International Journal of Computer Applications in Technology
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
A modified support vector machine and its application to image segmentation
Image and Vision Computing
A common framework for the convergence of the GSK, MDM and SMO algorithms
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
The robust and efficient adaptive normal direction support vector regression
Expert Systems with Applications: An International Journal
Credit risk evaluation with kernel-based affine subspace nearest points learning method
Expert Systems with Applications: An International Journal
First and Second Order SMO Algorithms for LS-SVM Classifiers
Neural Processing Letters
SoftDoubleMinOver: a simple procedure for maximum margin classification
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
L2-SVM: Dependence on the regularization parameter
Pattern Recognition and Image Analysis
INFORMS Journal on Computing
Maximum Margin One Class Support Vector Machines for multiclass problems
Pattern Recognition Letters
Simple solvers for large quadratic programming tasks
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
MaxMinOver regression: a simple incremental approach for support vector function approximation
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
New approximation algorithms for minimum enclosing convex shapes
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
A modified SMO algorithm for SVM regression and its application in quality prediction of HP-LDPE
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Properties of the solution of L2-Support Vector Machine as a function of regularization parameter
Pattern Recognition and Image Analysis
Computers and Industrial Engineering
Neighbors' distribution property and sample reduction for support vector machines
Applied Soft Computing
| Hi-index | 0.01 |
In this paper we give a new fast iterative algorithm for support vector machine (SVM) classifier design. The basic problem treated is one that does not allow classification violations. The problem is converted to a problem of computing the nearest point between two convex polytopes. The suitability of two classical nearest point algorithms, due to Gilbert, and Mitchell et al., is studied. Ideas from both these algorithms are combined and modified to derive our fast algorithm. For problems which require classification violations to be allowed, the violations are quadratically penalized and an idea due to Cortes and Vapnik and Friess is used to convert it to a problem in which there are no classification violations. Comparative computational evaluation of our algorithm against powerful SVM methods such as Platt's sequential minimal optimization shows that our algorithm is very competitive