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- W195393342 abstract "AbstractKnown are many different capacity measures for learning machines like: Vapnik-Chervonenkis dimension, covering numbers or fat dimension. In this paper we present experimental results of sample complexity estimation, taking into account rather simple learning machines linear in parameters. We show that, sample complexity can be quite different even for learning machines having the same VC-dimension. Moreover, independently from the capacity of a learning machine, the distribution of data is also significant. Experimental results are compared with known theoretical results for sample complexity and generalization bounds.KeywordsMean Square ErrorSample ComplexityTrue ErrorBayesian RegularisationCapacity ConceptThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W195393342 date "2012-01-01" @default.
- W195393342 modified "2023-10-03" @default.
- W195393342 title "Sample Complexity of Linear Learning Machines with Different Restrictions over Weights" @default.
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- W195393342 doi "https://doi.org/10.1007/978-3-642-29350-4_13" @default.
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