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We address the problem of nding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the deenitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present deeni-tions(More)
  • Scott Sherwood Benson, Nils J Nilsson, Yoav Shoham, Barbara Hayes-Roth, Marko Balabanovic, Lise Getoor +24 others
  • 1996
ii I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, a s a dissertation for the degree of Doctor of Philosophy. I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, a s a dissertation for the degree of Doctor of Philosophy. I certify that(More)
We present MLC ++ , a library of C ++ classes and tools for supervised Machine Learning. While MLC ++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and(More)
When mining large databases, the data extraction problem and the interface between the database and data mining algorithm become important issues. Rather than giving a mining algorithm full access to a database (by extracting to a at le or other directly-accessible data structure), we propose the SQL Interface Protocol (SIP), which is a framework for(More)