Generalization performance of Lagrangian support vector machine based on Markov sampling

DOI10.1016/j.jspi.2020.09.001zbMath1465.62144OpenAlexW3108828978MaRDI QIDQ830752

JingJing Zeng, Desheng Wang, Bin Zou, Yuze Duan, Yue Yin, Jie Xu

Publication date: 7 May 2021

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2020.09.001

zbMATH Keywords

strongly mixinggeneralization boundi.i.d.Lagrangian support vector machines (LSVM)Markov sampling

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05) Markov processes: estimation; hidden Markov models (62M05)

Cites Work

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• Rates of convergence for empirical processes of stationary mixing sequences
• Learning and generalisation. With applications to neural networks.
• Statistical behavior and consistency of classification methods based on convex risk minimization.
• The generalization performance of ERM algorithm with strongly mixing observations
• Regularization networks and support vector machines
• Learning rates of regularized regression for exponentially strongly mixing sequence
• Learning rates of least-square regularized regression
• On the mathematical foundations of learning
• Support Vector Machines
• Consistency of Support Vector Machines and Other Regularized Kernel Classifiers
• Online Regularized Classification Algorithms
• 10.1162/15324430152748218
• 10.1162/153244302760200713
• Uniform ergodicity and strong mixing
• Theory of Reproducing Kernels

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