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» Graph-based induction and its applications
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NIPS
2007
13 years 9 months ago
Anytime Induction of Cost-sensitive Trees
Machine learning techniques are increasingly being used to produce a wide-range of classifiers for complex real-world applications that involve nonuniform testing costs and miscl...
Saher Esmeir, Shaul Markovitch
JMLR
2002
102views more  JMLR 2002»
13 years 7 months ago
Efficient Algorithms for Decision Tree Cross-validation
Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward...
Hendrik Blockeel, Jan Struyf
PTS
2010
132views Hardware» more  PTS 2010»
13 years 6 months ago
Increasing Functional Coverage by Inductive Testing: A Case Study
This paper addresses the challenge of generating test sets that achieve functional coverage, in the absence of a complete specification. The inductive testing technique works by p...
Neil Walkinshaw, Kirill Bogdanov, John Derrick, Ja...
DASFAA
2007
IEEE
220views Database» more  DASFAA 2007»
14 years 2 months ago
LAPIN: Effective Sequential Pattern Mining Algorithms by Last Position Induction for Dense Databases
Sequential pattern mining is very important because it is the basis of many applications. Although there has been a great deal of effort on sequential pattern mining in recent year...
Zhenglu Yang, Yitong Wang, Masaru Kitsuregawa
AAAI
2006
13 years 9 months ago
Anytime Induction of Decision Trees: An Iterative Improvement Approach
Most existing decision tree inducers are very fast due to their greedy approach. In many real-life applications, however, we are willing to allocate more time to get better decisi...
Saher Esmeir, Shaul Markovitch