C. Elkan. Boosting and naive bayesian learning [http://www-cse.ucsd.edu/#elkan/papers/bnb.ps]. Department of Computer Science and Engineering, Univ. of California, San Diego, CA, 1997. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Pruning Meta-Classifiers in a Distributed Data Mining System - Prodromidis, Stolfo (1998) (7 citations) (Correct)
....that associates a specific cost model tailored to the credit card fraud detection problem. 4 Experiments and Results Learning algorithms Five inductive learning algorithms are used in our experiments. ID3, its successor C4.5, and Cart [3] are decision tree based algorithms, Bayes, described in [11], is a naive Bayesian classifier and Ripper [9] is a rule induction algorithm. Learning tasks Two data sets of real credit card transactions were used in our experiments provided by the Chase and First Union Banks, members of the FSTC (Financial Services Technology Consortium) The two data sets ....
C. Elkan. Boosting and naive bayesian learning [http://www-cse.ucsd.edu/#elkan/papers/bnb.ps]. Department of Computer Science and Engineering, Univ. of California, San Diego, CA, 1997.
On the Management of Distributed Learning Agents - Prodromidis (1997) (Correct)
....k randomly chosen subsets, and each of which is used in turn as the test set while the rest form the training set. Learning algorithms Five inductive learning algorithms are used in our experiments. ID3, its successor C4.5 [38] and Cart are decision tree based algorithms, Bayes, described in [16], is a naive bayesian classifier that is based on computing conditional probabilities, and Ripper [13] is a rule induction algorithm based on IREP [1] Learning tasks Two data sets of real credit card transactions and two molecular biology sequence analysis data sets, were used in our ....
C. Elkan. Boosting and naive bayesian learning [http://www-cse.ucsd.edu/¸elkan/papers/bnb.ps]. Department of Computer Science and Engineering, Univ. of California, San Diego, CA, 1997.
Mining databases with different schemas: Integrating.. - Prodromidis (1998) (1 citation) (Correct)
....we describe in detail our experiments in combining seemingly incompatible classification models using these techniques. Learning algorithms Five inductive learning algorithms are used in our experiments. ID3, its successor C4.5, and Cart [2] are decision tree based algorithms, Bayes, described in [8], is a naive Bayesian classifier and Ripper [6] is a rule induction algorithm based on IREP [9] Learning tasks Two data sets of real credit card transactions were used in our experiments provided by the Chase and First Union Banks, members of the FSTC (Financial Services Technology Consortium) ....
C. Elkan. Boosting and naive bayesian learning [http://wwwcse. ucsd.edu/¸elkan/papers/bnb.ps]. Department of Computer Science and Engineering, Univ. of California, San Diego, CA, 1997.
Pruning Classifiers in a Distributed Meta-Learning System - Prodromidis, Stolfo, Chan (1998) (3 citations) (Correct)
....j ; c k ) 3. coverage and a cost model tailored to the credit card fraud detection problem. 4 Experiments and Results Learning algorithms Five inductive learning algorithms are used in our experiments. ID3, its successor C4.5, and Cart [2] are decision tree based algorithms, Bayes, described in [10], is a naive Bayesian classifier and Ripper [8] is a rule induction algorithm. Learning tasks Two data sets of real credit card transactions were used in our experiments provided by the Chase and First Union Banks, members of the FSTC (Financial Services Technology Consortium) These two data sets ....
C. Elkan. Boosting and naive bayesian learning [http://wwwcse. ucsd.edu/¸elkan/papers/bnb.ps]. Department of Computer Science and Engineering, Univ. of California, San Diego, CA, 1997.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC