Ruben Ramirez-Padron

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EEG spike and wave (SW) activity has been described through a non-parametric stochastic model estimated by the Nadaraya-Watson (NW) method. In this paper the performance of the NW, the local linear polynomial regression and support vector machines (SVM) methods were compared. The noise-free realizations obtained by the NW and SVM methods reproduced SW(More)
Outlier detection is an important research topic that focuses on detecting abnormal information in data sets and processes. This paper addresses the problem of determining which class of kernels should be used in a geometric framework for nearest neighbor-based outlier detection. It introduces the class of similarity kernels and employs it within that(More)
Genetic algorithms (GAs) are probabilistic search techniques inspired by natural evolution. Selection schemes are used by GAs to choose individuals from a population to breed the next generation. Proportionate, ranking and tournament selection are standard selection schemes. They focus on choosing individuals with high fitness values. Fitness Uniform(More)
Gaussian processes (GPs) have been shown to be highly effective for novelty detection through the use of different membership scores. However, applications of GPs to novelty detection have been limited only to batch GP, which require all training data at once and have quadratic space complexity and cubic time complexity. This paper proposes the use of(More)
Support Vector Machine (SVM) training is equivalent to solving a large constrained optimization problem. Much work has been spent on decompositional optimization methods for this problem, but non-decompositional approaches have only recently regained attention. Notably, Sentelle’s work in applying Rusin’s revised simplex method to SVM training demonstrated(More)
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