A Library for Locally Weighted Projection Regression

Stefan Klanke; Sethu Vijayakumar; Stefan Schaal

A Library for Locally Weighted Projection Regression

Stefan Klanke, Sethu Vijayakumar, Stefan Schaal

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

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.

Original language	English
Pages (from-to)	623-626
Number of pages	4
Journal	Journal of Machine Learning Research
Volume	9
Publication status	Published - Apr 2008

Keywords / Materials (for Non-textual outputs)

regression
local learning
online learning
C
Matlab
C++
Octave
Python

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@article{9130d7fe54c0470e80735e66ce37ebd3,

title = "A Library for Locally Weighted Projection Regression",

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.",

keywords = "regression, local learning, online learning, C, Matlab, C++, Octave, Python",

author = "Stefan Klanke and Sethu Vijayakumar and Stefan Schaal",

year = "2008",

month = apr,

language = "English",

volume = "9",

pages = "623--626",

journal = "Journal of Machine Learning Research",

issn = "1532-4435",

publisher = "Microtome Publishing",

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TY - JOUR

T1 - A Library for Locally Weighted Projection Regression

AU - Klanke, Stefan

AU - Vijayakumar, Sethu

AU - Schaal, Stefan

PY - 2008/4

Y1 - 2008/4

N2 - 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.

AB - 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.

KW - regression

KW - local learning

KW - online learning

KW - C

KW - Matlab

KW - C++

KW - Octave

KW - Python

M3 - Article

SN - 1532-4435

VL - 9

SP - 623

EP - 626

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

ER -

A Library for Locally Weighted Projection Regression

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Keywords / Materials (for Non-textual outputs)

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