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• W2100835923 abstract "Successful inductive learning requires that training data be expressed in a form where underlying regularities can be recognized by the learning system. Unfortunately, many applications of inductive learning/spl minus/especially in the domain of molecular biology/spl minus/have assumed that data are provided in a form already suitable for learning, whether or not such an assumption is actually justified. This paper describes the use of background knowledge of molecular biology to re-express data into a form more appropriate for learning. Our results show dramatic improvements in classification accuracy for two very different classes of DNA sequences using traditional off-the-sheIf decision-tree and neural-network inductive-learning methods. >" @default.
• W2100835923 created "2016-06-24" @default.
• W2100835923 creator A5000665967 @default.
• W2100835923 creator A5089280215 @default.
• W2100835923 date "2002-12-17" @default.
• W2100835923 modified "2023-09-25" @default.
• W2100835923 title "Using background knowledge to improve inductive learning of DNA sequences" @default.
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• W2100835923 doi "https://doi.org/10.1109/caia.1994.323654" @default.
• W2100835923 hasPublicationYear "2002" @default.
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