Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김용대 | - |
dc.date.accessioned | 2017-11-22T06:30:05Z | - |
dc.date.available | 2017-11-22T06:30:05Z | - |
dc.date.issued | 2003 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.other | OAK-18072 | - |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/239208 | - |
dc.description.abstract | A new noise robust ensemble method called "Averaged Boosting (A-Boosting)" is proposed. Using the hypothetical ensemble algorithm in Hilbert space, we explain that A-Boosting can be understood as a method of constructing a sequence of hypotheses and coefficients such that the average of the product of the base hypotheses and coefficients converges to the desirable function. Empirical studies showed that A-Boosting outperforms Bagging for low noise cases and is more robust than AdaBoost to label noise. | - |
dc.description.sponsorship | Korea Advanced Institute of Science and Technology, AITRC;Seoul National University, SRCCS;Korea Information Science Society;Korean Datamining Society | - |
dc.language | English | - |
dc.title | Averaged boosting: A noise-robust ensemble method | - |
dc.type | Conference Paper | - |
dc.relation.volume | 2637 | - |
dc.relation.index | SCOPUS | - |
dc.relation.startpage | 388 | - |
dc.relation.lastpage | 393 | - |
dc.relation.journaltitle | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) | - |
dc.identifier.scopusid | 2-s2.0-7444257989 | - |
dc.author.google | Kim Y. | - |
dc.date.modifydate | 20180104081001 | - |