Title :
MLC++: a machine learning library in C++
Author :
Kohavi, Ron ; John, George ; Long, Richard ; Manley, David ; Pfleger, Karl
Author_Institution :
Dept. of Comput. Sci., Stanford Univ., CA, USA
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 :
C language; learning (artificial intelligence); software reliability; C++; MLC++; algorithm development; comparison tools; machine learning library; software reliability; Acceleration; Computer science; Data mining; Displays; Libraries; Machine learning; Machine learning algorithms; Programming profession; Testing; Tree graphs;
Conference_Titel :
Tools with Artificial Intelligence, 1994. Proceedings., Sixth International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-8186-6785-0
DOI :
10.1109/TAI.1994.346412