Abstract
Here is presented CAMLET that is a platform for automatic composition of inductive applications with method repositories that organize many inductive learning methods. CAMLET starts with constructing a basic design specification for inductive applications with method repositories and data type hierarchy that are specific to inductive learning algorithms. After instantiating the basic design with a given data set into a detailed design specification and then compiling it into codes, CAMLET executes them on computers. CAMLET changes the constructed specification until it goes beyond the goal accuracy given from a user. After having implemented CAMLET on UNIX platforms with Perl and C languages, we have done the case studies of constructing inductive applications for eight different data sets from the StatLog project and have compared the accuracies of the inductive applications composed by CAMLET with all the accuracies from popular inductive learning algorithms. The results have shown us that the inductive applications composed by CAMLET take the first accuracy on the average.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 576-585 |
| Number of pages | 10 |
| Volume | 2281 |
| Publication status | Published - 2002 |
| Externally published | Yes |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 2281 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Constructing inductive applications by meta-learning with method repositories. / Abe, Hidenao; Yamaguchi, Takahira.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2281 2002. p. 576-585 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2281).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Constructing inductive applications by meta-learning with method repositories
AU - Abe, Hidenao
AU - Yamaguchi, Takahira
PY - 2002
Y1 - 2002
N2 - Here is presented CAMLET that is a platform for automatic composition of inductive applications with method repositories that organize many inductive learning methods. CAMLET starts with constructing a basic design specification for inductive applications with method repositories and data type hierarchy that are specific to inductive learning algorithms. After instantiating the basic design with a given data set into a detailed design specification and then compiling it into codes, CAMLET executes them on computers. CAMLET changes the constructed specification until it goes beyond the goal accuracy given from a user. After having implemented CAMLET on UNIX platforms with Perl and C languages, we have done the case studies of constructing inductive applications for eight different data sets from the StatLog project and have compared the accuracies of the inductive applications composed by CAMLET with all the accuracies from popular inductive learning algorithms. The results have shown us that the inductive applications composed by CAMLET take the first accuracy on the average.
AB - Here is presented CAMLET that is a platform for automatic composition of inductive applications with method repositories that organize many inductive learning methods. CAMLET starts with constructing a basic design specification for inductive applications with method repositories and data type hierarchy that are specific to inductive learning algorithms. After instantiating the basic design with a given data set into a detailed design specification and then compiling it into codes, CAMLET executes them on computers. CAMLET changes the constructed specification until it goes beyond the goal accuracy given from a user. After having implemented CAMLET on UNIX platforms with Perl and C languages, we have done the case studies of constructing inductive applications for eight different data sets from the StatLog project and have compared the accuracies of the inductive applications composed by CAMLET with all the accuracies from popular inductive learning algorithms. The results have shown us that the inductive applications composed by CAMLET take the first accuracy on the average.
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M3 - Chapter
AN - SCOPUS:26844475845
SN - 3540433384
SN - 9783540433385
VL - 2281
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 576
EP - 585
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -