Abstract
We present an experiment in which an information retrieval system usinga forest of decision trees was trained using Utgoff’s ITI algorithm on two test collections. The system was then compared with a conventional inverted indexing engine and found to give a superior performance. We argue that the method has the potential to be used in real applications where the task domain is homogeneous.
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© 2002 Springer-Verlag Berlin Heidelberg
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Sutcliffe, R.F.E., White, K. (2002). The Feasibility of Machine Learning for Query Answering — An Experiment in Two Domains. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds) Artificial Intelligence and Cognitive Science. AICS 2002. Lecture Notes in Computer Science(), vol 2464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45750-X_15
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DOI: https://doi.org/10.1007/3-540-45750-X_15
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