In statisticallearning bounds on generalization error and sample complexities are important elements. In the paper we compare several selected generalization bounds having in mind their practical applications. In particular; we state twa theorems which compare bounds derived via additive and multiplicative versions of Chemoff inequality. In experimental part we show (using a benchmark data set) how one can practically apply bounds and sample complexity.
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