The nature of statistical learning theory
The nature of statistical learning theory
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Support Vector Data Description
Machine Learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel class-wise locality preserving projection
Information Sciences: an International Journal
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
Application of smoothing technique on twin support vector machines
Pattern Recognition Letters
Nonparallel plane proximal classifier
Signal Processing
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
TSVR: An efficient Twin Support Vector Machine for regression
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
Twin Mahalanobis distance-based support vector machines for pattern recognition
Information Sciences: an International Journal
Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction
IEEE Transactions on Knowledge and Data Engineering
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
Structural twin parametric-margin support vector machine for binary classification
Knowledge-Based Systems
Nonparallel hyperplane support vector machine for binary classification problems
Information Sciences: an International Journal
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This paper formulates a twin-hypersphere support vector machine (THSVM) classifier for binary recognition. Similar to the twin support vector machine (TWSVM) classifier, this THSVM determines two hyperspheres by solving two related support vector machine (SVM)-type problems, each one is smaller than the classical SVM, which makes the THSVM be more efficient than the classical SVM. In addition, the THSVM avoids the matrix inversions in its two dual quadratic programming problems (QPPs) compared with the TWSVM. By considering the characteristics of the dual QPPs of THSVM, an efficient Gilbert's algorithm for the THSVM based on the reduced convex hull (RCH) instead of directly optimizing its pair of QPPs is further presented. Computational results on several synthetic as well as benchmark datasets indicate the significant advantages of the THSVM classifier in the computational time and test accuracy.