—In kernel based regression techniques (such as Support Vector Machines or Least Squares Support Vector Machines) it is hard to analyze the influence of perturbed inputs on the ...
Abstract. Two novel Natural Language Processing (NLP) classification techniques are applied to the analysis of corporate annual reports in the task of financial forecasting. The ...
Abstract. The present work is focused on a global image characterization based on a description of the 2D displacements of the different shapes present in the image, which can be e...
Edoardo Ardizzone, Antonio Chella, Roberto Pirrone
The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave minimiza...
In this paper1 we propose a new algorithm for providing confidence and credibility values for predictions on a multi-class pattern recognition problem which uses Support Vector mac...
We propose a Genetic Algorithm (GA) approach combined with Support Vector Machines (SVM) for the classification of high dimensional Microarray data. This approach is associated to ...
In this paper we present an analysis of the minimal hardware precision required to implement Support Vector Machine (SVM) classification within a Logarithmic Number System archite...
Faisal M. Khan, Mark G. Arnold, William M. Potteng...
We investigate a method using support vector machines (SVMs) with walk-based graph kernels for high-level feature extraction from images. In this method, each image is first segme...
Abstract. The use of support vector machines for multi-category problems is still an open field to research. Most of the published works use the one-against-rest strategy, but with...
In this paper we introduce XCSF with support vector prediction: the problem of learning the prediction function is solved as a support vector regression problem and each classifie...