MLC++: a machine learning library in C++
- 17 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 740-743
- https://doi.org/10.1109/tai.1994.346412
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 can attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible. In this paper we discuss the problems MLC++ aims to solve, the design of MLC++, and the current functionality.Keywords
This publication has 7 references indexed in Scilit:
- Irrelevant Features and the Subset Selection ProblemPublished by Elsevier ,1994
- Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and ExperimentsMachine Learning, 1994
- A technique for drawing directed graphsIEEE Transactions on Software Engineering, 1993
- Very Simple Classification Rules Perform Well on Most Commonly Used DatasetsMachine Learning, 1993
- Computational learning theoryPublished by Association for Computing Machinery (ACM) ,1992
- Instance-based learning algorithmsMachine Learning, 1991
- Induction of decision treesMachine Learning, 1986