Rademacher Complexity and Grammar Induction Algorithms: What It May (Not) Tell Us

Author:

Katrenko Sophia,van Zaanen Menno

Publisher

Springer Berlin Heidelberg

Reference5 articles.

1. Bartlett, P.L., Mendelson, S.: Rademacher and gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research 3 (2002)

2. Bubeck, S., von Luxburg, U.: Nearest neighbor clustering: A baseline method for consistent clustering with arbitrary objective functions. JMLR 10, 657–698 (2009)

3. Clark, A.: Unsupervised Language Acquisition: Theory and Practice. PhD thesis, COGS, University of Sussex (2001)

4. van Zaanen, M., Adriaans, P.: Alignment-Based Learning versus EMILE: A Comparison. In: Proceedings of the Belgian-Dutch Conference on Artificial Intelligence (BNAIC), pp. 315–322 (2001)

5. Lecture Notes in Artificial Intelligence;M. Zaanen van,2008

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1. Complexity-Based Methods;Model Selection and Error Estimation in a Nutshell;2019-07-18

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3. Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates;Neural Processing Letters;2015-05-08

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