Dlib-ml: A machine learning toolkit. There are many excellent toolkits which provide support for developing machine learning software in Python, R, Matlab, and similar environments. Dlib-ml is an open source library, targeted at both engineers and research scientists, which aims to provide a similarly rich environment for developing machine learning software in the C++ language. Towards this end, dlib-ml contains an extensible linear algebra toolkit with built in BLAS support. It also houses implementations of algorithms for performing inference in Bayesian networks and kernel-based methods for classification, regression, clustering, anomaly detection, and feature ranking. To enable easy use of these tools, the entire library has been developed with contract programming, which provides complete and precise documentation as well as powerful debugging tools

References in zbMATH (referenced in 12 articles )

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  1. Qin, Wanting; Tang, Jun; Lu, Cong; Lao, Songyang: Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an example (2021)
  2. van Bulck, David; Goossens, Dries: Handling fairness issues in time-relaxed tournaments with availability constraints (2020)
  3. Wang, Ting; Leiter, Kenneth W.; Plecháč, Petr; Knap, Jaroslaw: Accelerated scale bridging with sparsely approximated Gaussian learning (2020)
  4. Van Bulck, David; Goossens, Dries R.; Spieksma, Frits C. R.: Scheduling a non-professional indoor football league: a tabu search based approach (2019)
  5. Gudivada, Venkat N.; Arbabifard, Kamyar: Open-source libraries, application frameworks, and workflow systems for NLP (2018)
  6. Maxime Rousseau; Jean-Marc Retrouvey: pfla: A Python Package for Dental Facial Analysis using Computer Vision and Statistical Shape Analysis (2018) not zbMATH
  7. Minary, Pauline; Pichon, Frédéric; Mercier, David; Lefevre, Eric; Droit, Benjamin: Face pixel detection using evidential calibration and fusion (2017)
  8. Adi, Yossi; Keshet, Joseph: StructED: risk minimization in structured prediction (2016)
  9. Beheshti, Behdad; Prokopyev, Oleg A.; Pasiliao, Eduardo L.: Exact solution approaches for bilevel assignment problems (2016)
  10. Chandar, Sarath; Khapra, Mitesh M.; Larochelle, Hugo; Ravindran, Balaraman: Correlational neural networks (2016)
  11. Müller, Andreas C.; Behnke, Sven: Pystruct-learning structured prediction in Python (2014)
  12. King, Davis E.: Dlib-ml: a machine learning toolkit (2009) ioport