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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  1. Marcelo S. Reis, Gustavo Estrela, Carlos Eduardo Ferreira, Junior Barrera: featsel: A framework for benchmarking of feature selection algorithms and cost functions (2017) not zbMATH
  2. Macià, Núria; Bernadó-Mansilla, Ester: Towards UCI+: a mindful repository design (2014) ioport
  3. Kelner, Roy; Lerner, Boaz: Learning Bayesian network classifiers by risk minimization (2012)
  4. Sierra, B.; Lazkano, E.; Irigoien, I.; Jauregi, E.; Mendialdua, I.: (K) nearest neighbor equality: giving equal chance to all existing classes (2011) ioport
  5. Bouckaert, Remco R.; Frank, Eibe; Hall, Mark A.; Holmes, Geoffrey; Pfahringer, Bernhard; Reutemann, Peter; Witten, Ian H.: WEKA -- experiences with a Java open-source project (2010)
  6. Terlecki, Paweı: On the relation between jumping emerging patterns and rough set theory with application to data classification (2010)
  7. Yehezkel, Raanan; Lerner, Boaz: Bayesian network structure learning by recursive autonomy identification (2009)
  8. Hu, Xiao-Peng; Dempere-Marco, Laura; Davies, E. Roy: Bayesian feature evaluation for visual saliency estimation (2008)
  9. Alcobé, Josep Roure: Learning Bayesian networks with an approximated MDL score (2007)
  10. Peña, Jose M.; Nilsson, Roland; Björkegren, Johan; Tegnér, Jesper: Towards scalable and data efficient learning of Markov boundaries (2007)
  11. Ramamohanarao, Kotagiri; Fan, Hongjian: Patterns based classifiers (2007) ioport
  12. De Campos, Luis M.: A scoring function for learning Bayesian networks based on mutual information and conditional independence tests (2006)
  13. Langseth, Helge; Nielsen, Thomas D.: Classification using hierarchical Naïve Bayes models (2006)
  14. Langseth, Helge; Nielsen, Thomas D.: Classification using hierarchical naïve Bayes models (2006) ioport
  15. Acid, Silvia; Campos, Luis M.; Castellano, Javier G.: Learning Bayesian network classifiers: Searching in a space of partially directed acyclic graphs (2005)
  16. Hutter, Marcus; Zaffalon, Marco: Distribution of mutual information from complete and incomplete data (2005)
  17. Langseth, Helge; Nielsen, Thomas D.: Latent classification models (2005)
  18. Li, Jinyan; Dong, Guozhu; Ramamohanarao, Kotagiri; Wong, Limsoon: DeEPs: A new instance-based lazy discovery and classification system (2004)
  19. Acid, S.; de Campos, L. M.: Searching for Bayesian network structures in the space of restricted acyclic partially directed graphs (2003)
  20. Brazdil, Pavel B.; Soares, Carlos; Pinto da Costa, Joaquim: Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results (2003)

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