Least Squares Support Vector Machine Classifiers
Neural Processing Letters
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
Survival-Time Classification of Breast Cancer Patients
Computational Optimization and Applications
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extended support vector interval regression networks for interval input-output data
Information Sciences: an International Journal
Information Sciences: an International Journal
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Extracting rules for classification problems: AIS based approach
Expert Systems with Applications: An International Journal
Refinement of approximate domain theories by knowledge-based neural networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Twin Mahalanobis distance-based support vector machines for pattern recognition
Information Sciences: an International Journal
A reduced support vector machine approach for interval regression analysis
Information Sciences: an International Journal
Probabilistic support vector machines for classification of noise affected data
Information Sciences: an International Journal
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We propose knowledge based versions of a relatively new family of SVM algorithms based on two non-parallel hyperplanes. Specifically, we consider prior knowledge in the form of multiple polyhedral sets and incorporate the same into the formulation of linear Twin SVM (TWSVM)/Least Squares Twin SVM (LSTWSVM) and term them as knowledge based TWSVM (KBTWSVM)/knowledge based LSTWSVM (KBLSTWSVM). Both of these formulations are capable of generating non-parallel hyperplanes based on real-world data and prior knowledge. We derive the solution of KBLSTWSVM and use it in our computational experiments for comparison against other linear knowledge based SVM formulations. Our experiments show that KBLSTWSVM is a versatile classifier whose solution is extremely simple when compared with other linear knowledge based SVM algorithms.