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.
| Author(s): | Klanke, S. and Vijayakumar, S. and Schaal, S. |
| Book Title: | Journal of Machine Learning Research |
| Volume: | 9 |
| Pages: | 623-626 |
| Year: | 2008 |
| Bibtex Type: | Article (article) |
| URL: | http://www-clmc.usc.edu/publications/k/klanke-JMLR2008.pdf |
| Cross Ref: | p10244 |
| Electronic Archiving: | grant_archive |
| Note: | clmc |
BibTex
@article{Klanke_JMLR_2008,
title = {A library for locally weighted projection regression},
booktitle = {Journal of Machine Learning Research},
abstract = {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.},
volume = {9},
pages = {623-626},
year = {2008},
note = {clmc},
slug = {klanke_jmlr_2008},
author = {Klanke, S. and Vijayakumar, S. and Schaal, S.},
crossref = {p10244},
url = {http://www-clmc.usc.edu/publications/k/klanke-JMLR2008.pdf}
}