A Survey on Inductive Learning
نویسندگان
چکیده
Inductive learning is the method of learning from observations. Inductive learning has important applications over a wide range of area including pattern recognition, language acquisition, bio-informatics and intelligent agent design. Because of such diverse applicability, inductive learning methods including automata learning, grammar induction, hidden markov model learning and symbolic statistical modeling have attracted the attention of researchers for several decades. Consequently, inductive learning continues to be a major focus of research. This report surveys some of the key results in automata learning, grammar induction, hidden markov model learning and symbolic statistical modeling. Different learning problems are critically compared to emphasize their usability for various applications. Learning from example sequences has been illustrated by implementing automata learning techniques to learn a process model to capture users browsing behavior in Web transactions. Experimental evaluation of the learned model is also reported. Possible future research is to incorporate grammar induction and symbolic statistical learning in rule based systems.
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