A new conic approach to semisupervised support vector machines

DOI10.1155/2016/6471672zbMath1400.68183OpenAlexW2299445215WikidataQ59140890 ScholiaQ59140890MaRDI QIDQ1793456

Ye Tian, Xin Yan, Jian Luo

Publication date: 12 October 2018

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2016/6471672

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• SeDuMi
• UCI-ml
• CVX

Cites Work

• Conic relaxations for semi-supervised support vector machines
• Computable representation of the cone of nonnegative quadratic forms over a general second-order cone and its application to completely positive programming
• Semidefinite programming versus the reformulation-linearization technique for nonconvex quadratically constrained quadratic programming
• A fast quasi-Newton method for semi-supervised SVM
• Detecting copositivity of a symmetric matrix by an adaptive ellipsoid-based approximation scheme
• On the copositive representation of binary and continuous nonconvex quadratic programs
• New SDP models for protein homology detection with semi-supervised SVM
• Introduction to Semi-Supervised Learning
• Some NP-complete problems in quadratic and nonlinear programming
• New Results on Quadratic Minimization
• Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
• On Cones of Nonnegative Quadratic Functions
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