Matrix computations (3rd ed.)
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Lagrangian support vector machines
The Journal of Machine Learning Research
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
Car assembly line fault diagnosis based on robust wavelet SVC and PSO
Expert Systems with Applications: An International Journal
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
Vector projection method for unclassifiable region of support vector machine
Expert Systems with Applications: An International Journal
Knowledge based Least Squares Twin support vector machines
Information Sciences: an International Journal
Localized twin SVM via convex minimization
Neurocomputing
Model selection for least squares support vector regressions based on small-world strategy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Distance difference and linear programming nonparallel plane classifier
Expert Systems with Applications: An International Journal
Generalized eigenvalue proximal support vector regressor
Expert Systems with Applications: An International Journal
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Expert Systems with Applications: An International Journal
Twin Mahalanobis distance-based support vector machines for pattern recognition
Information Sciences: an International Journal
A weighted twin support vector regression
Knowledge-Based Systems
Probabilistic outputs for twin support vector machines
Knowledge-Based Systems
1-Norm least squares twin support vector machines
Neurocomputing
Robust twin support vector machine for pattern classification
Pattern Recognition
A twin-hypersphere support vector machine classifier and the fast learning algorithm
Information Sciences: an International Journal
Twin support vector machine with Universum data
Neural Networks
Efficient Implementation of Nonparallel Hyperplanes Classifier
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Structural twin parametric-margin support vector machine for binary classification
Knowledge-Based Systems
Twin least squares support vector regression
Neurocomputing
Large-scale linear nonparallel support vector machine solver
Neural Networks
A nonparallel support vector machine for a classification problem with universum learning
Journal of Computational and Applied Mathematics
Least squares twin parametric-margin support vector machine for classification
Applied Intelligence
Smooth Newton method for implicit Lagrangian twin support vector regression
International Journal of Knowledge-based and Intelligent Engineering Systems
Extending twin support vector machine classifier for multi-category classification problems
Intelligent Data Analysis
Hi-index | 12.06 |
In this paper we formulate a least squares version of the recently proposed twin support vector machine (TSVM) for binary classification. This formulation leads to extremely simple and fast algorithm for generating binary classifiers based on two non-parallel hyperplanes. Here we attempt to solve two modified primal problems of TSVM, instead of two dual problems usually solved. We show that the solution of the two modified primal problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in TSVM. Classification using nonlinear kernel also leads to systems of linear equations. Our experiments on publicly available datasets indicate that the proposed least squares TSVM has comparable classification accuracy to that of TSVM but with considerably lesser computational time. Since linear least squares TSVM can easily handle large datasets, we further went on to investigate its efficiency for text categorization applications. Computational results demonstrate the effectiveness of the proposed method over linear proximal SVM on all the text corpuses considered.