| 书目名称 | Change of Representation and Inductive Bias | | 编辑 | D. Paul Benjamin | | 视频video | | | 丛书名称 | The Springer International Series in Engineering and Computer Science | | 图书封面 |  | | 描述 | 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 dis | | 出版日期 | Book 1990 | | 关键词 | autonomous system; hierarchical planning; knowledge; learning; machine learning; problem solving; proving | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4613-1523-0 | | isbn_softcover | 978-1-4612-8817-6 | | isbn_ebook | 978-1-4613-1523-0Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Kluwer Academic Publishers 1990 |
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发表于 2025-3-21 19:38:52
