LWPR

A library for locally weighted projection regression. In this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the key features, our code supports multi-threading, is available for multiple platforms, and provides wrappers for several programming languages.


References in zbMATH (referenced in 7 articles , 1 standard article )

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  1. Tran, Huu-Toan; Cheng, Hong; Lin, Xichuan; Duong, Mien-Ka; Huang, Rui: The relationship between physical human-exoskeleton interaction and dynamic factors: using a learning approach for control applications (2014) ioport
  2. Gijsberts, Arjan; Metta, Giorgio: Real-time model learning using incremental sparse spectrum Gaussian process regression (2013)
  3. Ting, Kai Ming; Wells, Jonathan R.; Tan, Swee Chuan; Teng, Shyh Wei; Webb, Geoffrey I.: Feature-subspace aggregating: ensembles for stable and unstable learners (2011) ioport
  4. Mitrovic, Djordje; Klanke, Stefan; Vijayakumar, Sethu: Adaptive optimal feedback control with learned internal dynamics models (2010)
  5. Sala√ľn, Camille; Padois, Vincent; Sigaud, Olivier: Learning forward models for the operational space control of redundant robots (2010)
  6. Plagemann, Christian; Mischke, Sebastian; Prentice, Sam; Kersting, Kristian; Roy, Nicholas; Burgard, Wolfram: A Bayesian regression approach to terrain mapping and an application to legged robot locomotion (2009)
  7. Klanke, Stefan; Vijayakumar, Sethu; Schaal, Stefan: A library for locally weighted projection regression (2008)