SCG '94 Proceedings of the tenth annual symposium on Computational geometry
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Support Vector Machines for Classification in Nonstandard Situations
Machine Learning
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy multi-category proximal support vector classification via generalized eigenvalues
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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
A classification method based on generalized eigenvalue problems
Optimization Methods & Software - Systems Analysis, Optimization and Data Mining in Biomedicine
k-Means Has Polynomial Smoothed Complexity
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Multiclass Generalized Eigenvalue Proximal Support Vector Machines
CISIS '10 Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
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Supervised classification of data affected by noise or error, with unknown probability distribution, is a challenging task. To this extend, we propose the Fuzzy Regularized Eigenvalue Classifier, based on a recent technique to classify data in two or more classes. We compare the execution time and accuracy of the classifier with other de facto standard methods. With the adoption of a novel membership function, the classifier is capable to produce more accurate models that well compare with results obtained by other methods, and fuzzy weighting functions, on benchmark datasets.