Feb 28, 2002 · Some topics relevant to this special issue include, but are not restricted to: Theoretical foundations, algorithms, and implementations; Model ...
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The goal of this paper is to thoroughly review SVMs from the optimization point of view. Examining the many aspects of SVM optimization problems, it is useful ...
The basic idea behind the SVM principle is that input vectors are nonlinearly mapped into a high-dimensional feature space where the algorithm constructs a ...
Jan 11, 2016 · SVM can be used for classification (distinguishing between several groups or classes) and regression (obtaining a mathematical model to predict something).
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Issues such as overexposure, excessive shadows, and distracting debris, or with depth of field and focusing can all hinder the ability to extract meaningful ...
The support vector machine and voted perceptron algorithm are trained using these kernels for extracting relations from text. The authors present experimental ...
ML covers a wide range of learning algorithms, including classic ones such as linear regression, k-nearest neighbors, or decision trees, through support vector ...
Oct 22, 2024 · In this article, the authors modify the state-of-the-art Support Vector Machines to address this challenging classification problem suffering ...
We discuss a method of least squares support vector machines (LS-SVM), which has been extended to recurrent models and use in optimal control problems.
Oct 18, 2019 · The papers use various ML techniques, including artificial neural networks, Gaussian processes, reinforcement learning, clustering techniques, ...