Results 31 to 40 of about 326,273 (311)

AGE ESTIMATION WITH REGARD FOR CLASSIFIABLE ABILITY OF EACH COMPONENT IN REDUCED DIMENSION AGE MANIFOLD [PDF]

ICTACT Journal on Image and Video Processing, 2018
A new age estimation method that takes classifiable ability of each component in age manifold into account is considered. First, we analysis the age classification rate of each component in reduced dimension age manifold.
Pak Duho, Ri Kumhyok, Hyon Cungyong
doaj   +1 more source

Incremental Sparse Density-Weighted Twin Support Vector Regression [PDF]

Jisuanji gongcheng
The Density-Weighted Twin Support Vector Regression(DWTSVR) is a regression algorithm that reflects the internal distribution of data with high prediction accuracy and robustness.
Weijie DING, Binjie GU, Feng PAN
doaj   +1 more source

Support vector regression for speaker verification [PDF]

Interspeech 2007, 2007
Proceedings of Interspeech 2007, Antwerp (Belgium) This paper explores Support Vector Regression (SVR) as an alternative to the widely-used Support Vector Classification (SVC) in GLDS (Generalized Linear Discriminative Sequence)-based speaker verification. SVR allows the use of a ε-insensitive loss function which presents many advantages.
Ignacio López-Moreno, Ismael Mateos-Garcia, Daniel Ramos 0001, Joaquin Gonzalez-Rodriguez   +3 more
openaire   +2 more sources

Extending twin support vector machine classifier for multi-category classification problems [PDF]

, 2013
© 2013 – IOS Press and the authors. All rights reservedTwin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems.
Liu, X, Xie, J, Gao, X, Shi, Y, Hone, K, Xie, W   +5 more
core   +1 more source

Ship Track Regression Based on Support Vector Machine

IEEE Access, 2017
This work was supported in part by the National Natural Science Foundation of China under Grant 61473331, in part by the Natural Science Foundation of Guangdong Province of China under Grant 2014A030307049, in part by the Ordinary University Innovation ...
Bo Ban, Junjie Yang, Pengguang Chen, Jianbin Xiong, Qinruo Wang   +4 more
doaj   +1 more source

Support Vector Regression [PDF]

, 2015
Rooted in statistical learning or Vapnik-Chervonenkis (VC) theory, support vector machines (SVMs) are well positioned to generalize on yet-to-be-seen data. The SVM concepts presented in Chapter 3 can be generalized to become applicable to regression problems.
Mariette Awad, Rahul Khanna
openaire   +1 more source

Clusterwise support vector linear regression

European Journal of Operational Research, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kaisa Joki, Adil M. Bagirov, Napsu Karmitsa, Marko M. Mäkelä, Sona Taheri   +4 more
openaire   +3 more sources

Support Vector Machines in R

, 2005
Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language.
Kurt Hornik, David Meyer, Karatzoglou, Alexandros, Hornik, Kurt, Meyer, David, Alexandros Karatzoglou   +5 more
core   +1 more source

Tau acetylation at K331 has limited impact on tau pathology in vivo

FEBS Letters, EarlyView.
We mapped tau post‐translational modifications in humanized MAPT knock‐in mice and in amyloid‐bearing double knock‐in mice. Acetylation within the repeat domain, particularly around K331, showed modest increases under amyloid pathology. To test functional relevance, we generated MAPTK331Q knock‐in mice.
Shoko Hashimoto, Yukio Matsuba, Mika Takahashi, Takaomi C. Saido   +3 more
wiley   +1 more source

Novel Feature Selection Method for Nonlinear Support Vector Regression

Complexity, 2022
The development of sparse techniques presents a major challenge to complex nonlinear high-dimensional data. In this paper, we propose a novel feature selection method for nonlinear support vector regression, called FS-NSVR, which first attempts to solve ...
Kejia Xu, Ying Xu, Yafen Ye, Weijie Chen
doaj   +1 more source