C. Campbell, N. Cristianini, и A. Smola. Proceedings of the 17th International Conference on Machine Learing,
(ICML2000, Stanford, CA, 2000), стр. 8. (2000)
Аннотация
The active selection of instances can significantly improve the generalisation
performance of a learning machine. Large margin classifiers such
as Support Vector Machines classify data using the most informative
instances (the support vectors). This makes them natural candidates
for instance selection strategies. In this paper we propose an algorithm
for the training of Support Vector Machines using instance selection.
We give a theoretical justification for the strategy and experimental
results on real and artificial data demonstrating its effectiveness.
The technique is most efficient when thedataset can be learnt using
few support vectors.
%0 Conference Paper
%1 campbell00query
%A Campbell, C.
%A Cristianini, N.
%A Smola, A.
%B Proceedings of the 17th International Conference on Machine Learing,
(ICML2000, Stanford, CA, 2000)
%D 2000
%K imported
%P 8
%T Query Learning with Large Margin Classifiers
%U /papers/upload_13546_icml.ps
%X The active selection of instances can significantly improve the generalisation
performance of a learning machine. Large margin classifiers such
as Support Vector Machines classify data using the most informative
instances (the support vectors). This makes them natural candidates
for instance selection strategies. In this paper we propose an algorithm
for the training of Support Vector Machines using instance selection.
We give a theoretical justification for the strategy and experimental
results on real and artificial data demonstrating its effectiveness.
The technique is most efficient when thedataset can be learnt using
few support vectors.
@inproceedings{campbell00query,
abstract = {The active selection of instances can significantly improve the generalisation
performance of a learning machine. Large margin classifiers such
as Support Vector Machines classify data using the most informative
instances (the support vectors). This makes them natural candidates
for instance selection strategies. In this paper we propose an algorithm
for the training of Support Vector Machines using instance selection.
We give a theoretical justification for the strategy and experimental
results on real and artificial data demonstrating its effectiveness.
The technique is most efficient when thedataset can be learnt using
few support vectors.},
added-at = {2008-04-30T12:59:47.000+0200},
author = {Campbell, C. and Cristianini, N. and Smola, A.},
biburl = {https://www.bibsonomy.org/bibtex/272c241d7164d122d6b10c793f205ebde/kdubiq},
booktitle = {Proceedings of the 17th International Conference on Machine Learing,
(ICML2000, Stanford, CA, 2000)},
description = {KDubiq Blueprint},
groupsearch = {0},
interhash = {fa3fc15545bb7717f695df0d238259e3},
intrahash = {72c241d7164d122d6b10c793f205ebde},
keywords = {imported},
pages = 8,
timestamp = {2008-04-30T13:00:12.000+0200},
title = {Query Learning with Large Margin Classifiers},
url = {/papers/upload_13546_icml.ps},
year = 2000
}