New Support Vector Algorithms
TLDR
A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.Abstract:
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of ν, and report experimental results.read more
Citations
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Journal ArticleDOI
A new predictive model for the filtered volume and outlet parameters in micro-irrigation sand filters fed with effluents using the hybrid PSO-SVM-based approach
P.J. García Nieto,Esperanza García-Gonzalo,Gerard Arbat,M. Duran-Ros,F. Ramírez de Cartagena,Jaume Puig-Bargués +5 more
TL;DR: The aim of this study was to obtain a predictive model able to perform an early detection of the filtered volume and sand filter outlet values of dissolved oxygen (DO) and turbidity, both related to emitter clogging risks.
4D Cardiff Conversation Database (4D CCDb): A 4D database of natural,dyadic conversations
TL;DR: This paper describes the data collection and annotation process, and provides results of a baseline classification experiment distinguishing frontchannel from backchannel smiles, using 3D Active Appearance Models for feature extraction, polynomial fitting for representing the data as 4D sequences, and Support Vector Machines for classification.
Journal ArticleDOI
On robust asymmetric Lagrangian ν-twin support vector regression using pinball loss function
Deepak Gupta,Umesh Gupta +1 more
TL;DR: In this article, a robust asymmetric Lagrangian ν -twin support vector regression using pinball loss function (URALTSVR) is proposed as a pair of the unconstrained minimization problem to handle not only the noise sensitivity and instability of re-sampling but also consist positive definite matrices.
Book ChapterDOI
Lagrangian Twin-Bounded Support Vector Machine Based on L2-Norm
Umesh Gupta,Deepak Gupta +1 more
TL;DR: A strongly convex objective function is constructed for proposed Lagrangian twin bounded support vector machine (LTBSVM) in consideration with L2-norm of the vector of slack variables in place of L1-norm vector of the slack variable.
Journal ArticleDOI
Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS.
Tatiana Fomina,Gabriele Lohmann,Michael Erb,Thomas Ethofer,Bernhard Schölkopf,Moritz Grosse-Wentrup +5 more
TL;DR: The results establish that ALS patients can employ self-regulation of precuneus oscillations for communication and a novel BCI designed for patients in late stages of ALS based on high-level cognitive processes that are less likely to be affected by ALS is introduced.
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