The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Mixed-integer programming for kernel-based classifiers
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Knowledge-Based multiclass support vector machines applied to vertical two-phase flow
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
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This paper presents a knowledge-based kernel classification model for binary classification of sets or objects with prior knowledge. The prior knowledge is in the form of multiple polyhedral sets belonging to one or two classes, and it is introduced as additional constraints into a regularized knowledge-based optimization problem. The resulting formulation leads to a least squares problem that can be solved using matrix or iterative methods. To evaluate the model, the experimental laminar & turbulent flow data and the Reynolds number equation used as prior knowledge were used to train and test the proposed model.