Sun, 27 Jul 2008 08:11:54 BST CiteULike: gabgas's learning http://www.citeulike.org/user/gabgas/tag/learning CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/gabgas/article/2178367 <i>Journal of Mathematical Psychology, Vol. 51, No. 3. (June 2007), pp. 135-163.</i><br /><br />Gold's [1967. Language identification in the limit. Information and Control, 16, 447-474] celebrated work on learning in the limit has been taken, by many cognitive scientists, to have powerful negative implications for the learnability of language from positive data (i.e., from mere exposure to linguistic input). This provides one, of several, lines of argument that language acquisition must draw on other sources of information, including innate constraints on learning. We consider an `ideal learner' that applies a Simplicity Principle to the problem of language acquisition. The Simplicity Principle chooses the hypothesis that provides the briefest representation of the available data--here, the data are the linguistic input to the child. The Simplicity Principle allows learning from positive evidence alone, given quite weak assumptions, in apparent contrast to results on language learnability in the limit (e.g., Gold, 1967). These results provide a framework for reconsidering the learnability of various aspects of natural language from positive evidence, which has been at the center of theoretical debate in research on language acquisition and linguistics. `Ideal learning' of natural language: Positive results about learning from positive evidence Nick Chater Paul Vitanyi doi:10.1016/j.jmp.2006.10.002 Journal of Mathematical Psychology, Vol. 51, No. 3. (June 2007), pp. 135-163. 2007-12-29T01:25:29-00:00 2007 Journal of Mathematical Psychology 51 3 135 163 language learning natural unsupervised http://www.citeulike.org/user/gabgas/article/2194597 <i>ACM Computing Surveys, Vol. 33, No. 1. (2001), pp. 1-30.</i><br /><br />Many tasks require "reasoning" --- i.e., deriving conclusions from a corpus of explicitly stored information --- to solve their range of problems. An ideal reasoning system would produce alland -only the correct answers to every possible query, produce answers that are as specific as possible, be expressive enough to permit any possible fact to be stored and any possible query to be asked, and be efficient. Unfortunately, this is provably impossible: as correct and precise systems become more... Efficient reasoning Russell Greiner Christian Darken Iwan Santoso ACM Computing Surveys, Vol. 33, No. 1. (2001), pp. 1-30. 2008-01-04T13:50:05-00:00 2001 ACM Computing Surveys 33 1 1 30 learning logic machine probabilistic http://www.citeulike.org/user/gabgas/article/1271098 <i>(2001)</i><br /><br />this paper, we will flesh out this remark by discussing the following topics: An introduction to graphical models K Murphy (2001) 2007-05-02T07:32:01-00:00 2001 learning machine