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Abstract: We present MLC++ , a library of C++ classes and tools for supervised Machine Learning. While MLC++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and display information visually. More than just a collection of existing algorithms, MLC++ is an attempt to extract commonalities of algorithms... (Update)
Context of citations to this paper: More
.... in the Machine Learning encourage the empirical evaluation of algorithms in their application domains (see comments in [19] and [15], among others) This further motivates the approach presented in this paper. 1.1. Previous work The problem of selecting the right...
...is a sand region or not, depends on the output of several sand region classifiers. We use the bagging technique and tools implemented in [10]. In that approach, several classifiers vote on the class of the input pattern and the final decision is made depending on the majority...
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BibTeX entry: (Update)
Kohavi, R., John, G., Long, R., Manley, D. and Pfleger, K. (1994) "MLC++: A Machine Learning Library in C++," Tech Report, Computer Science Dept, Stanford University. http://citeseer.ist.psu.edu/article/kohavi94mlc.html More
@misc{ kohavi94mlc,
author = "R. Kohavi and G. John and R. Long and D. Manley and K. Pfleger",
title = "MLC++: A Machine Learning Library in C",
text = "Kohavi, R., John, G., Long, R., Manley, D. and Pfleger, K. (1994) MLC++:
A Machine Learning Library in C++, Tech Report, Computer Science Dept, Stanford
University.",
year = "1994",
url = "citeseer.ist.psu.edu/article/kohavi94mlc.html" }
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