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Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech
(MDPI, 2016-01)
In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an ...
Evaluation of two virtual cursors for assisting web access to people with motor impairments
(Elsevier, 2019-08-02)
People with motor impairments (MI) may face accessibility barriers when using computers due to their health conditions and therefore need to use alternative devices to a standard mouse for pointing and clicking in graphical ...
Smart Meeting Room Usage Information and Prediction by Modelling Occupancy Profiles
(MDPI, 2019-01-02)
The monitoring of small houses and rooms has become possible due to the advances in IoT sensors, actuators and low power communication protocols in the last few years. As buildings are one of the biggest energy consuming ...
Dynamic selection of the best base classifier in one versus one
(Elsevier, 2015-05-19)
Class binarization strategies decompose the original multi-class problem into several binary sub-problems. One versus One (OVO) is one of the most popular class binarization techniques, which considers every pair of classes ...
NewOneVersusOneAll method: NOV@
(Elsevier, 2014-04-19)
Binarization strategies decompose the original multi-class dataset into multiple two-class subsets, learning a different binary model for each new
subset. One-vs-All (OVA) and One-vs-One (OVO) are two of the most well-known ...
Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms
(Elsevier, 2015-01-24)
This paper proposes a novel approach to select the individual classifiers to take part in a Multiple-Classifier System. Individual classifier selection is a key step in the development of multi-classifiers. Several works ...
A Survey of Performance Modeling and Simulation Techniques for Accelerator-Based Computing
(IEEE, 2014-02-25)
The high performance computing landscape is shifting from collections of homogeneous nodes towards heterogeneous systems, in which nodes consist of a combination of traditional out-of-order execution cores and accelerator ...
An efficient implementation of kernel density estimation for multi-core and many-core architectures
(Sage, 2015-03-16)
Kernel density estimation (KDE) is a statistical technique used to estimate the probability density function of a sample set with unknown density function. It is considered a fundamental data-smoothing problem for use with ...
Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations
(2015-01-01)
We propose an extension to multiple dimensions of the univariate index of agreement between Probability Density Functions (PDFs) used in climate studies. We also provide a set of high-performance programs targeted both to ...
Kernel density estimation in accelerators: Implementation and performance evaluation
(ACM, 2016-02-01)
Kernel density estimation (KDE) is a popular technique used to estimate the probability density function of a random variable. KDE is considered a fundamental data smoothing algorithm, and it is a common building block in ...