IEICE TRANSACTIONS on Information
Learning from Expert Hypotheses and Training Examples
Summary :
We present a method for learning classification functions from pre-classified training examples and hypotheses written roughly by experts. The goal is to produce a classification function that has higher accuracy than either the expert's hypotheses or the classification function inductively learned from the training examples alone. The key idea in our proposed approach is to let the expert's hypotheses influence the process of learning inductively from the training examples. Experimental results are presented demonstrating the power of our approach in a variety of domains.
- Publication
- IEICE TRANSACTIONS on Information Vol.E80-D No.12 pp.1205-1214
- Publication Date
- 1997/12/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Artificial Intelligence and Cognitive Science
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Shigeo KANEDA, Hussein ALMUALLIM, Yasuhiro AKIBA, Megumi ISHII, "Learning from Expert Hypotheses and Training Examples" in IEICE TRANSACTIONS on Information,
vol. E80-D, no. 12, pp. 1205-1214, December 1997, doi: .
Abstract: We present a method for learning classification functions from pre-classified training examples and hypotheses written roughly by experts. The goal is to produce a classification function that has higher accuracy than either the expert's hypotheses or the classification function inductively learned from the training examples alone. The key idea in our proposed approach is to let the expert's hypotheses influence the process of learning inductively from the training examples. Experimental results are presented demonstrating the power of our approach in a variety of domains.
URL: https://global.ieice.org/en_transactions/information/10.1587/e80-d_12_1205/_p
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@ARTICLE{e80-d_12_1205,
author={Shigeo KANEDA, Hussein ALMUALLIM, Yasuhiro AKIBA, Megumi ISHII, },
journal={IEICE TRANSACTIONS on Information},
title={Learning from Expert Hypotheses and Training Examples},
year={1997},
volume={E80-D},
number={12},
pages={1205-1214},
abstract={We present a method for learning classification functions from pre-classified training examples and hypotheses written roughly by experts. The goal is to produce a classification function that has higher accuracy than either the expert's hypotheses or the classification function inductively learned from the training examples alone. The key idea in our proposed approach is to let the expert's hypotheses influence the process of learning inductively from the training examples. Experimental results are presented demonstrating the power of our approach in a variety of domains.},
keywords={},
doi={},
ISSN={},
month={December},}
Copy
TY - JOUR
TI - Learning from Expert Hypotheses and Training Examples
T2 - IEICE TRANSACTIONS on Information
SP - 1205
EP - 1214
AU - Shigeo KANEDA
AU - Hussein ALMUALLIM
AU - Yasuhiro AKIBA
AU - Megumi ISHII
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E80-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 1997
AB - We present a method for learning classification functions from pre-classified training examples and hypotheses written roughly by experts. The goal is to produce a classification function that has higher accuracy than either the expert's hypotheses or the classification function inductively learned from the training examples alone. The key idea in our proposed approach is to let the expert's hypotheses influence the process of learning inductively from the training examples. Experimental results are presented demonstrating the power of our approach in a variety of domains.
ER -
English
