David Garcia Chaparro

  • Citations Per Year
Learn More
The electromyography (EMG) reflects the electrical behavior generated by muscles movements. According the movement, muscles fibers are activated and the EMG signal reflects changes of different electromagnetic components of these fibers in time. In this paper, we describe the methodology applied for the detection of fiber muscle activation, which required(More)
Support Vector-based learning methods are an important part of Computational Intelligence techniques. Recent efforts have been dealing with the problem of learning from very large datasets. This paper reviews the most commonly used formulations of support vector machines for regression (SVRs) aiming to emphasize its usability on large-scale applications. We(More)
Minimally invasive techniques that introduce cement and bone substitutes inside the fractured vertebral body are a new treatment line with clinically proven efficacy. However, mechanical behaviours between different fillers throughout fracture evolution is yet to be clarified, as many substances are available for introduction into the vertebral body(More)
In this paper we study the problem of model selection for a linear programming-based support vector machine for regression. We propose generalized method that is based on a quasi-Newton method that uses a globalization strategy and an inexact computation of first order information. We explore the case of two-class, multi-class, and regression problems.(More)
This research studies short-term electricity load prediction with a large-scalelinear programming support vector regression (LP-SVR) model. The LP-SVR is compared with other three non-linear regression models: Collobert’s SVR, FeedForward Neural Networks (FFNN), and Bagged Regression Trees (BRT). The four models are trained to predict hourly day-ahead loads(More)
  • 1