Knowledge Acquisition from Databases
A comprehensive English-Russian and Russian-English collection of modern statistical terminology, containing some 13,500 terms and some 1,000 names. Topics covered include mathematical statistics and probability theory, computational statistics and statistical software, and applied statistical components in economics, sociology, demography, medicine, natural sciences, and technology. The volume provides an extensive collection of terms in the fields of computer terminology related to problems of data processing and statistical software, theory of random processes, statistical quality control, operations research, and some supplementary areas such as the terminology of Russian official statistics. For translators and other experts who work with English and Russian statistical literature. Annotation copyright by Book News, Inc., Portland, OR
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Learning From Databases
Constructing Decision Trees With ID3 and C4 5
Generating Rules With HCV
Dealing With Noise and RealValued Attributes
A Performance Comparison of HCV With IDS C4 5
A Representation for Integrating Knowledge and Data
An Intelligent Learning Database System
B Results Produced by HCV on the MONKs Problems
An Example Run of SIKT in KEshell
acquisition 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
Page 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.