Fuzzy sets and their application to clustering and training
Fuzzy sets and their application to clustering and training
AI Game Programming Wisdom
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Outlier Detection Algorithms in Data Mining Systems
Programming and Computing Software
The Journal of Machine Learning Research
A hybrid method for patterns mining and outliers detection in the web usage log
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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An adaptive algorithm to solve a wide range of problems of unsupervised learning by constructing a sequence of interrelated extremal principles was proposed. The least squares method with a priori defined weights used as a starting point enabled determination of the "center" of learning sample. Next, a natural passage from the least squares method to more flexible extremal principle enabling adaptive determination of both the "center" and weights of the learning sample events was performed. Finally, a universal extremal principle enabling determination of the scaling coefficient of the membership function in addition to the "center" and weights was constructed.