Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Statistical Learning Theory: A Primer
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Visualizing asymmetric proximities with SOM and MDS models
Neurocomputing
An experimental study on asymmetric self-organizing map
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
From indefinite to positive semi-definite matrices
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
k-Means clustering of asymmetric data
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
On the informativeness of asymmetric dissimilarities
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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The aim of this paper is to afford classification tasks on asymmetric kernel matrices using Support Vector Machines (SVMs). Ordinary theory for SVMs requires to work with symmetric proximity matrices. In this work we examine the performance of several symmetrization methods in classification tasks. In addition we propose a new method that specifically takes classification labels into account to build the proximity matrix. The performance of the considered method is evaluated on a variety of artificial and real data sets.