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Abstract: In this paper, we propose meta-learning as a general technique to combine the results of multiple learning algorithms, each applied to a set of training data. We detail several metalearning strategies for combining independently learned classifiers, each computed by different algorithms, to improve overall prediction accuracy. The overall resulting classifier is composed of the classifiers generated by the different learning algorithms and a meta-classifier generated by a meta-learning... (Update)
Context of citations to this paper: More
...in a rule is desired. Such objective measures is important as analyst are often unsure of their input parameters. Meta learning [5, 32] within the framework to adjust parameters such as the support and confidence would be an important future work. We discussed seven...
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BibTeX entry: (Update)
P. Chan, S. Stolfo. Experiments on multistrategy learning by meta-learning. In 2nd Intl. Conf. on Info. and Knowledge Mgmt., Nov 1993. http://citeseer.ist.psu.edu/chan93experiments.html More
@inproceedings{ chan93experiments,
author = "Philip K. Chan and Salvatore J. Stolfo",
title = "Experiments in Multistrategy Learning by Meta-Learning",
booktitle = "Proceedings of the second international conference on information and knowledge management",
address = "Washington, DC",
pages = "314--323",
year = "1993",
url = "citeseer.ist.psu.edu/chan93experiments.html" }
Citations (may not include all citations):1359 Induction of decision trees (context) - Quinlan - 1986
1262 Classification and Regression Trees (context) - Breiman, Friedman et al. - 1984
537 A theory of the learnable (context) - Valiant - 1984
367 Stacked generalization - Wolpert - 1992
273 The strength of weak learnability - Schapire - 1990
233 The CN2 induction algorithm - Clark, Niblett - 1987
180 Boosting a weak learning algorithm by majority - Freund - 1990
171 A weighted nearest neighbor algorithm for learning with symb.. - Cost, Salzberg - 1993
130 Refinement of approximate domain theories by knowledge-based.. - Towell, Shavlik et al. - 1990
70 Predicting the secondary structure of globular proteins usin.. (context) - Qian, Sejnowski - 1988
68 A hybrid system for protein secondary structure prediction (context) - Zhang, Mesirov et al. - 1992
59 Boosting performance in neural networks (context) - Drucker, Schapire et al. - 1993
59 Toward parallel and distributed learning by meta-learning - Chan, Stolfo - 1993
54 Meta-learning for multistrategy and parallel learning (context) - Chan, Stolfo - 1993
48 Systems for knowledge discovery in databases - Matheus, Chan et al. - 1993
44 Training knowledge-based neural networks to recognize genes .. - Noordewier, Towell et al. - 1991
32 Introduction to IND and Recursive Partitioning (context) - Buntine, Caruana - 1991
11 ILS: A framework for multi-paradigmatic learning (context) - Silver, Frawley et al. - 1990
10 The human genome project (context) - DeLisi - 1988
8 Toward multistrategy parallel and distributed learning in se.. (context) - Chan, Stolfo - 1993
7 The need for biases in learning generalizaions (context) - Mitchell - 1980
6 Speech recognition in parallel (context) - Stolfo, Galil et al. - 1989
4 High performance computing and communications for grand chal.. (context) - Wah - 1993
2 A study of explanationbased mehtods for inductive learning (context) - Flann, Dietterich - 1989
The graph only includes citing articles where the year of publication is known.
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