MLC++
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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References in zbMATH (referenced in 41 articles )
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- De Campos, Luis M.: A scoring function for learning Bayesian networks based on mutual information and conditional independence tests (2006)
- Langseth, Helge; Nielsen, Thomas D.: Classification using hierarchical Naïve Bayes models (2006)
- Langseth, Helge; Nielsen, Thomas D.: Classification using hierarchical naïve Bayes models (2006)
- Acid, Silvia; Campos, Luis M.; Castellano, Javier G.: Learning Bayesian network classifiers: Searching in a space of partially directed acyclic graphs (2005)
- Hutter, Marcus; Zaffalon, Marco: Distribution of mutual information from complete and incomplete data (2005)
- Langseth, Helge; Nielsen, Thomas D.: Latent classification models (2005)
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- Acid, S.; de Campos, L. M.: Searching for Bayesian network structures in the space of restricted acyclic partially directed graphs (2003)
- Brazdil, Pavel B.; Soares, Carlos; Pinto da Costa, Joaquim: Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results (2003)