Fast Gaussian kernel learning for classification tasks based on specially structured global optimization

DOI10.1016/j.neunet.2014.05.014zbMath1325.68206OpenAlexW2039025536WikidataQ50655654 ScholiaQ50655654MaRDI QIDQ889286

Shangping Zhong, Tianshun Chen, Yuzhen Niu, Fengying He

Publication date: 6 November 2015

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2014.05.014

zbMATH Keywords

difference of convex functionsdifference of increasing functionsfast Gaussian kernel learning methodHoffman's outer approximation methodkernel target alignment criterionspecially structured global optimization

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Efficient learning of supervised kernels with a graph-based loss function

Uses Software

• UCI-ml
• LIBSVM
• Delve Datasets
• Delve Utilities

Cites Work

• Optimizing the Gaussian kernel function with the formulated kernel target alignment criterion for two-class pattern classification
• A review of recent advances in global optimization
• An efficient kernel matrix evaluation measure
• Fast rates for support vector machines using Gaussian kernels
• Gaussian kernel optimization for pattern classification
• Improving Newton-Raphson method for nonlinear equations by modified Adomian decomposition method
• Generalization performance of Gaussian kernels SVMC based on Markov sampling
• A method for globally minimizing concave functions over convex sets
• Gradient-Based Adaptation of General Gaussian Kernels
• Introduction to global optimization.
• Convexification, concavification and monotonization in global optimization
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