Structural multiple empirical kernel learning

DOI10.1016/J.INS.2015.01.005zbMath1360.68718OpenAlexW2028317605MaRDI QIDQ528736

Daqi Gao, Zhe Wang, Sheng Ke, Qi Fan

Publication date: 16 May 2017

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2015.01.005

zbMATH Keywords

pattern recognitionRademacher complexitystructural learningmultiple kernel learningcluster informationempirical kernel mapping

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (1)

Improving localized multiple kernel learning via radius-margin bound

Uses Software

• SHOGUN
• UCI-ml
• JStatCom
• KEEL
• AS 136

Cites Work

• Algorithm AS 136: A K-Means Clustering Algorithm
• Scaling the kernel function based on the separating boundary in input space: a data-dependent way for improving the performance of kernel methods
• Novel Fisher discriminant classifiers
• Design of a multiple kernel learning algorithm for LS-SVM by convex programming
• Regularizing multiple kernel learning using response surface methodology
• Empirical margin distributions and bounding the generalization error of combined classifiers
• Kernel class-wise locality preserving projection
• Quasiconformal kernel common locality discriminant analysis with application to breast cancer diagnosis
• Ho–Kashyap classifier with generalization control
• Rademacher penalties and structural risk minimization
• Canonical Correlation Analysis: An Overview with Application to Learning Methods
• Online Learning with (Multiple) Kernels: A Review
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
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