Machine Learning
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Computational Power of Winner-Take-All
Neural Computation
Gender Classification of Human Faces
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Sentiment Classification with Support Vector Machines and Multiple Kernel Functions
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Content-enriched classifier for web video classification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Detecting Management Fraud in Public Companies
Management Science
Improved support vector machine generalization using normalized input space
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Proceedings of the 2013 International Conference on Software Engineering
Structural twin parametric-margin support vector machine for binary classification
Knowledge-Based Systems
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This article deals with various aspects of normalization in the context of Support Vector Machines. We consider fist normalization of the vectors in the input space and point out the inherent limitations. A natural extension to the feature space is then represented by the kernel function normalization. A correction of the position of the Optimal Separating Hyperplane is subsequently introduced so as to suit better these normalized kernels. Numerical experiments finally evaluate the different approaches.