[PDF][PDF] Survey of classification algorithms and various model selection methods
V Sharma - J. Mach. Learn. Res, 2000 - academia.edu
This report describes in a comprehensive manner the various types of classification
algorithms that already exist. I will mainly be discussing and comparing in detail the major 7 …
algorithms that already exist. I will mainly be discussing and comparing in detail the major 7 …
[PDF][PDF] A generic framework for rule-based classification
A Giacometti, EK Miyaneh, P Marcel… - Proceedings of LeGo, 2008 - info.univ-tours.fr
Classification is an important field of data mining problems. Given a set of labeled training
examples the classification task constructs a classifier. A classifier is a global model which is …
examples the classification task constructs a classifier. A classifier is a global model which is …
Combination of classifiers
A combination or an ensemble of classifiers is a set of classifiers whose individual decisions
are combined to classify new examples. A combination of classifiers is often much more …
are combined to classify new examples. A combination of classifiers is often much more …
[图书][B] Advances in Learning Classifier Systems: Third International Workshop, IWLCS 2000, Paris, France, September 15-16, 2000. Revised Papers
PL Lanzi, W Stolzmann, SW Wilson - 2003 - books.google.com
Learning classi er systems are rule-based systems that exploit evolutionary c-putation and
reinforcement learning to solve di cult problems. They were-troduced in 1978 by John H …
reinforcement learning to solve di cult problems. They were-troduced in 1978 by John H …
A methodology for scaling up classification algorithms
B Boutsinas, G Prassas… - International Journal on …, 2004 - World Scientific
Contrary to much of the research in machine learning where there is a concentration on
problems with relatively small volume of data, one of the main challenges of the today's data …
problems with relatively small volume of data, one of the main challenges of the today's data …
Machine learning: a review of classification and combining techniques
Supervised classification is one of the tasks most frequently carried out by so-called
Intelligent Systems. Thus, a large number of techniques have been developed based on …
Intelligent Systems. Thus, a large number of techniques have been developed based on …
Recursive automatic bias selection for classifier construction
CE Brodley - Machine Learning, 1995 - Springer
The results of empirical comparisons of existing learning algorithms illustrate that each
algorithm has a selective superiority; each is best for some but not all tasks. Given a data set …
algorithm has a selective superiority; each is best for some but not all tasks. Given a data set …
Addressing the selective superiority problem: Automatic algorithm/model class selection
CE Brodley - Proceedings of the tenth international conference …, 1993 - books.google.com
The results of empirical comparisons of existing learning algorithms illustrate that each
algorithm has a selective superiority; it is best for some but not all tasks. Given a data set, it is …
algorithm has a selective superiority; it is best for some but not all tasks. Given a data set, it is …
The 2003 learning classifier systems bibliography
T Kovacs - International Workshop on Learning Classifier Systems, 2002 - Springer
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A bigger learning classifier systems bibliography
T Kovacs, P Luca Lanzi - International Workshop on Learning Classifier …, 2000 - Springer
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