Improving support vector machine classifiers by modifying kernal functions

TL;DR: A new family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets are introduced that can be used in shallow architectures, such as support vector machines (SVMs), or in deep kernel-based architectures that the authors call multilayers kernel machines (MKMs).

TL;DR: The role of the AUV Sirius on a research cruise to survey drowned reefs along the shelf edge of the Great Barrier Reef in Queensland, Australia was described in this paper, where the primary function was to provide georeferenced, high-resolution optical validation of seabed interpretations based on acoustic data.

TL;DR: If the data are noiseless, the modified version of basis pursuit denoising proposed in this article is equivalent to SVM in the following sense: if applied to the same data set, the two techniques give the same solution, which is obtained by solving the same quadratic programming problem.

TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.

TL;DR: Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.

TL;DR: Rifamycin compounds having high antibacterial activity, consisting of powder colored from yellow to orange.