Results 101 to 110 of about 108,016 (310)
Facial expression recognition using three-stage support vector machines
Visual Computing for Industry, Biomedicine, and Art, 2019 Herein, a three-stage support vector machine (SVM) for facial expression recognition is proposed. The first stage comprises 21 SVMs, which are all the binary combinations of seven expressions.
Issam Dagher +2 more
doaj +1 more source
Benchmarking least squares support vector machine classifiers. [PDF]
In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a ( convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function
Suykens, Johan +7 more
core
In Situ Micromechanical Study of Bimodal γ′–γ″ Precipitate Assemblies in Ni–Cr–Al–Nb Superalloy
Advanced Engineering Materials, EarlyView.A Ni–Cr–Al–Nb superalloy with a bimodal γ′–γ″ precipitate distribution is developed. Composite precipitate assemblies form through heterogeneous nucleation, effectively impeding dislocation motion. Micropillar compression reveals high strength at room and elevated temperatures, governed by precipitate shearing, with coupled faulting mechanisms ...
Ujjval Bansal +4 more
wiley +1 more source
Support Vector Machine and Generalization
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2004 The support vector machine (SVM) has been extended to build up nonlinear classifiers using the kernel trick. As a learning model, it has the best recognition performance among the many methods currently known because it is devised to obtain high performance for unlearned data.
openaire +1 more source
Catenary Support Vector Machines [PDF]
, 2008 Many problems require making sequential decisions. For these problems, the benefit of acquiring further information must be weighed against the costs. In this paper, we describe the catenary support vector machine(catSVM), a margin-based method to solve sequential stopping problems.
Kin Fai Kan, Christian R. Shelton
openaire +1 more source
Detection of Cochlear Hearing Loss Applying Wavelet Packets and Support Vector Machines
, 2004 The aim of this paper is to evaluate the application of the wavelet packet transform (WP) and support vector machines (SVM) to transient evoked otoacoustic emissions (TEOAE) in order to achieve a detection of frequency-specific hearing loss. We introduce
Weiss, Stephan +5 more
core +1 more source
Infinite Ensemble Learning with Support Vector Machines [PDF]
, 2005 Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of base learners. However, existing algorithms are limited to combining only a finite number of base learners, and the generated ensemble is ...
Lin, Hsuan-Tien
core +1 more source
Advanced Engineering Materials, EarlyView.This study shows that superalloys used in aircraft engine disks become much more prone to deformation at high temperatures if they have been strained during manufacturing. This effect increases with the level of prior strain but eventually reaches a limit.
Fabio Machado Alves da Fonseca +9 more
wiley +1 more source
Compressed Support Vector Machines
CoRR, 2015 Support vector machines (SVM) can classify data sets along highly non-linear decision boundaries because of the kernel-trick. This expressiveness comes at a price: During test-time, the SVM classifier needs to compute the kernel inner-product between a test sample and all support vectors.
Zhixiang Eddie Xu +3 more
openaire +2 more sources
An Optimized Method for Semi-supervised Support Vector Machines
Gong-kuang zidonghua, 2010 In view of problem of non-convex optimization problem that semi-supervised support vector machines use margin maximization principle to classify labeled and unlabeled samples, a method EDA_S3VM was proposed which using estimation of distribution ...
WANG Yong +3 more
doaj