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The Study of Leave-One-Out Error-Based Classification Learning Algorithm for Generalization Performance

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Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4221))

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Abstract

This note mainly focuses on a theoretical analysis of the generalization ability of classification learning algorithm. The explicit bound is derived on the relative difference between the generalization error and leave-one-out error for classification learning algorithm under the condition of leave-one-out stability by using Markov’s inequality, and then this bound is used to estimate the generalization error of classification learning algorithm. We compare the result in this paper with previous results in the end.

Supported in part by NSFC under grant 60403011.

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© 2006 Springer-Verlag Berlin Heidelberg

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Zou, B., Xu, J., Li, L. (2006). The Study of Leave-One-Out Error-Based Classification Learning Algorithm for Generalization Performance. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_2

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  • DOI: https://doi.org/10.1007/11881070_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45901-9

  • Online ISBN: 978-3-540-45902-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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