IEICE TRANSACTIONS on Information
Machine Learning via Multiresolution Approximation
Summary :
We consider the classification problem as a problem of approximation of a given training set. This approximation is constructed in a multiresolution framework, and organized in a tree-structure. It allows efficient training and query, both in constant time per training point. The proposed method is efficient for low-dimensional classification and regression estimation problems with large data sets.
- Publication
- IEICE TRANSACTIONS on Information Vol.E86-D No.7 pp.1172-1180
- Publication Date
- 2003/07/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section INVITED PAPER (Special Issue on Multiresolution Analysis)
- Category
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Ilya BLAYVAS, Ron KIMMEL, "Machine Learning via Multiresolution Approximation" in IEICE TRANSACTIONS on Information,
vol. E86-D, no. 7, pp. 1172-1180, July 2003, doi: .
Abstract: We consider the classification problem as a problem of approximation of a given training set. This approximation is constructed in a multiresolution framework, and organized in a tree-structure. It allows efficient training and query, both in constant time per training point. The proposed method is efficient for low-dimensional classification and regression estimation problems with large data sets.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_7_1172/_p
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@ARTICLE{e86-d_7_1172,
author={Ilya BLAYVAS, Ron KIMMEL, },
journal={IEICE TRANSACTIONS on Information},
title={Machine Learning via Multiresolution Approximation},
year={2003},
volume={E86-D},
number={7},
pages={1172-1180},
abstract={We consider the classification problem as a problem of approximation of a given training set. This approximation is constructed in a multiresolution framework, and organized in a tree-structure. It allows efficient training and query, both in constant time per training point. The proposed method is efficient for low-dimensional classification and regression estimation problems with large data sets.},
keywords={},
doi={},
ISSN={},
month={July},}
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TY - JOUR
TI - Machine Learning via Multiresolution Approximation
T2 - IEICE TRANSACTIONS on Information
SP - 1172
EP - 1180
AU - Ilya BLAYVAS
AU - Ron KIMMEL
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2003
AB - We consider the classification problem as a problem of approximation of a given training set. This approximation is constructed in a multiresolution framework, and organized in a tree-structure. It allows efficient training and query, both in constant time per training point. The proposed method is efficient for low-dimensional classification and regression estimation problems with large data sets.
ER -
English
