Classifier learning from noisy data as probabilistic evidence combination
作者： Haym Hirsh , Steven W. Norton
DOI:
关键词: Learning classifier system 、 Semi-supervised learning 、 Machine learning 、 Probabilistic logic 、 Maximum a posteriori estimation 、 Competitive learning 、 Unsupervised learning 、 Multi-task learning 、 Stability (learning theory) 、 Generalization error 、 Active learning (machine learning) 、 Wake-sleep algorithm 、 Computer science 、 Online machine learning 、 Instance-based learning 、 Artificial intelligence 、 Algorithmic learning theory 、 Pattern recognition
摘要: This paper presents an approach to learning from noisy data that views the problem as one of reasoning under uncertainty, where prior knowledge noise process is applied compute a posteriori probabilities over hypothesis space. In preliminary experiments this maximum (MAP) exhibits rate advantage C4.5 algorithm statistically significant.
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