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Common terms and phrasesacquisition from databases attribute value AUSCULTATION Chapter classified computing concept description conjunctive formula conjunctive rule continuous attribute data sets database technology decision tree deduction described discretization disjunction matrix domain theory example set expert systems explanation-based learning extension matrix approach factor fast strategy file_name forward chaining fuzzy has_tie=[no HCV algorithm HCV Version 2.0 heuristic ID3 algorithm ID3-like algorithms implementation induction algorithms inductive logic programming inevitable selectors inference input intersecting group jacket_color<>[red KEshell knowledge acquisition learning algorithms learning engine machine learning machine learning techniques method multiple match negative examples node noise handling Non-M2 class nondead elements normal NP-hard number of examples odd black cards OPS5 overfitting partition Plag Positive examples covered problem produced by HCV Prolog pruning relational database representation rows rule body rule induction rule schema rule set rules produced schemata Specify switch test examples training examples training set tuples unsupervised learn version space Popular passagesPage 200 - Generating production rules from decision trees," Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp. 304-307, Morgan Kaufmann Publishers, San Mateo, CA, 1987. Page 100 - ... have become one of the most widely used models of knowledge representation in AI, in particular expert systems. Rather than expressing logic calculus about the world as in Prolog-like logic programming systems or computing the numeric values defined over data as in conventional programming, rule-based production systems normally determine how the symbol structures that represent the current state of the problem should be manipulated to bring the representation closer to a solution. Problems that... Page 199 - Variable- Valued Logic and Its Applications to Pattern Recognition and Machine Learning, Computer Science and Multiple- Valued Logic Theory and Applications, DC Rine (Ed.), Amsterdam: North-Holland, 1975, 506-534. [Michalski et al. Page 90 - ... {yes, no}. The learning task is a binary classification task. Each problem is given by a logical description of a class. Robots belong either to this class or not, but instead of providing a complete class description to the learning problem, only a subset of all 432 possible robots with its classification is given. The learning task is then to generalize over these examples and, if the particular learning technique at hand allows this, to derive a simple class description. Page 82 - The simplest way to solve the multiple match issue is to use the first rule which classifies the example to determine the example's classification. If the rules from induction have been sorted and ordered according to their reliability or their class reliability (eg putting rules related to the largest class before others), this simple method can be expected to produce reasonable results. The advantage of this method is that it is straightforward and efficient in execution time. However, the price... Page 81 - The rationale behind this method is that if the examples in the training set are representative, the possibility of a random example belonging to a large class is higher than that of it belonging to a small one. The largest class method is good when the number of classes in a training set is small and one of the classes contains a predominant number of examples. However, the results could deteriorate when the number of classes grows, and the number of examples in every class is more evenly spread... Page 90 - ... Each problem is given by a logical description of a concept. Robots belong to either this concept or not. but instead of providing a complete concept description to the learning problem, only a subset of all 432 possible robots with its classification is given. The learning task is then to generalize over these examples and, if the particular learning technique at hand allows this, to derive a simple concept description. After a concept description has been produced by a learning algorithm from... Page 153 - HCV is a heuristic attribute-based induction algorithm based on the newly-developed extension matrix approach. By dividing the positive examples (PE) of a specific class in a given example set into intersecting groups and adopting a set of strategies to find a heuristic conjunctive... Page 118 - ... interactive knowledge transfer program SIKT [Wu 95] is a Structured Interactive Knowledge Transfer program designed and implemented in KEshell. It can automatically build executable knowledge bases out of direct dialogue with domain experts. As the dialogue process is structurally engineered, a domain expert does not need to know much about knowledge engineering or programming languages. All the expert needs to do is answer the questions asked by SIKT. SIKT builds up a factor dictionary and a... Page 3 - Existing work on machine learning has concentrated in the main on inducing rules from unordered sets of examples, especially attribute-based induction, an inductive formalism in which examples are described in terms of a fixed collection of attributes. References to this bookFrom other books
From Google ScholarSiteHelper: a localized agent that helps incremental exploration ...Daniel Siaw Weng Ngu, Xindong Wu - 1997 - Computer Networks and ISDN Systems Eliminating Class Noise in Large DatasetsXingquan Zhu, Xindong Wu, Qijun Chen Class Noise vs. Attribute Noise: A Quantitative StudyXingquan Zhu, Xindong Wu - 2004 - Artificial Intelligence Review Basic Gene Grammars and DNA-ChartParser for language processing of ...Siu-wai Leung, Chris Mellish, Dave Robertson - 2001 - Bioinformatics References from web pagesKnowledge Acquisition from Databases - Wu (researchindex) Data Mining and Knowledge Discovery Nuggets 96:7, e-mailed 96-02-23 Knowledge Acquisition From Databases - Boek - BESLIST.nl CS 331/295 Syllabus by Week Building Intelligent Learning Database Systems Knowledge Acquisition from Databases Database Design & Programming Bibliographic information
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