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Change of Representation and Inductive Bias One of the most
important emerging concerns of machine learning researchers is the
dependence of their learning programs on the underlying
representations, especially on the languages used to describe
hypotheses. The effectiveness of learning algorithms is very
sensitive to this choice of language; choosing too large a language
permits too many possible hypotheses for a program to consider,
precluding effective learning, but choosing too small a language
can prohibit a program from being able to find acceptable
hypotheses. This dependence is not just a pitfall, however; it is
also an opportunity. The work of Saul Amarel over the past two
decades has demonstrated the effectiveness of representational
shift as a problem-solving technique. An increasing number of
machine learning researchers are building programs that learn to
alter their language to improve their effectiveness. At the Fourth
Machine Learning Workshop held in June, 1987, at the University of
California at Irvine, it became clear that the both the machine
learning community and the number of topics it addresses had grown
so large that the representation issue could not be discussed in
sufficient depth. A number of attendees were particularly
interested in the related topics of constructive induction, problem
reformulation, representation selection, and multiple levels of
abstraction. Rob Holte, Larry Rendell, and I decided to hold a
workshop in 1988 to discuss these topics. To keep this workshop
small, we decided that participation be by invitation only.
Change of Representation and Inductive Bias One of the most
important emerging concerns of machine learning researchers is the
dependence of their learning programs on the underlying
representations, especially on the languages used to describe
hypotheses. The effectiveness of learning algorithms is very
sensitive to this choice of language; choosing too large a language
permits too many possible hypotheses for a program to consider,
precluding effective learning, but choosing too small a language
can prohibit a program from being able to find acceptable
hypotheses. This dependence is not just a pitfall, however; it is
also an opportunity. The work of Saul Amarel over the past two
decades has demonstrated the effectiveness of representational
shift as a problem-solving technique. An increasing number of
machine learning researchers are building programs that learn to
alter their language to improve their effectiveness. At the Fourth
Machine Learning Workshop held in June, 1987, at the University of
California at Irvine, it became clear that the both the machine
learning community and the number of topics it addresses had grown
so large that the representation issue could not be discussed in
sufficient depth. A number of attendees were particularly
interested in the related topics of constructive induction, problem
reformulation, representation selection, and multiple levels of
abstraction. Rob Holte, Larry Rendell, and I decided to hold a
workshop in 1988 to discuss these topics. To keep this workshop
small, we decided that participation be by invitation only.
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