0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.03.023 q This project was supported by CONACyT unde scholarship 205834. ⇑ Corresponding author. Tel.: +52 2222663100x831 E-mail address: [email protected] (H.J. Escalant This article describes the application of particle swarm model selection (PSMS) to the problem of automatic image annotation (AIA). PSMS c...

In the context of motor imagery (MI)-based brain-computer interface (BCI) systems, a great amount research has been studied for attaining higher classification performance by extracting discriminative features from MI-based electroencephalogram (EEG) signals. this study, we propose an innovative approach classifying multi-class MI-EEG signals, which consists signal processing technique based on...

LIght Detection And Ranging (LIDAR) is a powerful remote sensing technology in the acquisition of the terrain surface information for object classification and extraction. Major benefits of this technique are its high level of automation during data capturing and its spatial resolution. Because of high complexities and difficulties in urban areas, many researchers focus on the using of LIDAR da...

BACKGROUND Multi-class molecular cancer classification has great potential clinical implications. Such applications require statistical methods to accurately classify cancer types with a small subset of genes from thousands of genes in the data. OBJECTIVES This paper presents a new functional gradient descent boosting algorithm that directly extends the HingeBoost algorithm from the binary ca...

We present a new algorithm for multi-class classification with multiple kernels. Our algorithm is based on a natural notion of the multi-class margin of a kernel. We show that larger values of this quantity guarantee the existence of an accurate multi-class predictor and also define a family of multiple kernel algorithms based on the maximization of the multi-class margin of a kernel (MK). We p...