SHARK. SHARK is an object-oriented library for the design of adaptive systems. It comprises methods for single- and multi-objective optimization (e.g., evolutionary and gradient-based algorithms) as well as kernel-based methods, neural networks, and other machine learning techniques.
Keywords for this software
References in zbMATH (referenced in 12 articles , 1 standard article )
Showing results 1 to 12 of 12.
- Doğan, Ürün; Glasmachers, Tobias; Igel, Christian: A unified view on multi-class support vector classification (2016)
- Fischer, Asja; Igel, Christian: Training restricted Boltzmann machines: an introduction (2014)
- Bringmann, Karl; Friedrich, Tobias; Igel, Christian; Voß, Thomas: Speeding up many-objective optimization by Monte Carlo approximations (2013)
- Brügge, Kai; Fischer, Asja; Igel, Christian: The flip-the-state transition operator for restricted Boltzmann machines (2013)
- Pulina, Luca; Tacchella, Armando: Challenging SMT solvers to verify neural networks (2012)
- Stallkamp, J.; Schlipsing, M.; Salmen, J.; Igel, C.: Man vs. Computer: benchmarking machine learning algorithms for traffic sign recognition (2012)
- Liefooghe, Arnaud; Jourdan, Laetitia; Talbi, El-Ghazali: A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO (2011)
- Pulina, Luca; Tacchella, Armando: NeVer: a tool for artificial neural networks verification (2011)
- Merelo Guervós, Juan Julián; Castillo, Pedro A.; Alba, Enrique: Algorithm::Evolutionary, a flexible Perl module for evolutionary computation (2010)
- Heidrich-Meisner, Verena; Igel, Christian: Neuroevolution strategies for episodic reinforcement learning (2009)
- Suttorp, Thorsten; Hansen, Nikolaus; Igel, Christian: Efficient covariance matrix update for variable metric evolution strategies (2009)
- Igel, Christian; Heidrich-Meisner, Verena; Glasmachers, Tobias: SHARK (2008)