Probably almost discriminative learning
作者:Kenji Yamanishi
摘要
This paper develops a new computational model for learning stochastic rules, called PAD (Probably Almost Discriminative)-learning model, based on statistical hypothesis testing theory. The model deals with the problem of designing a discrimination algorithm to test whether or not any given test sequence of examples of pairs of (instance, label) has come from a given stochastic ruleP*. Here a composite hypothesis\(\tilde P\) is unknown other than it belongs to a given classC.
论文关键词:Computational learning theory, universal hypothesis testing, stochastic rule, PAD-learning, MDL principle
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论文官网地址:https://doi.org/10.1007/BF00993820