MLC++: a machine learning library in C++. 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

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  5. Fan, Hongjian; Kotagiri, Ramamohanarao: An efficient single-scan algorithm for mining essential jumping emerging patterns for classification (2002)
  6. Hilario, Melanie: Model complexity and algorithm selection in classification (2002)
  7. Valentini, Giorgio; Masulli, Francesco: NEURObjects: an object-oriented library for neural network development (2002)
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  9. Brazdil, Pavel; Soares, Carlos; Pereira, Rui: Reducing rankings of classifiers by eliminating redundant classifiers (2001)
  10. Dhar, Vasant: A comparison of GLOWER and other machine learning methods for investment decision making (2001)
  11. Hilario, Melanie; Kalousis, Alexandros: Fusion of meta-knowledge and meta-data for case-based model selection (2001)
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