NDLI: Least Squares Conditional Density Estimation in semi-supervised learning settings

Content Provider	IEEE Xplore Digital Library
Author	Khan, R.R. Sugiyama, M.
Copyright Year	2012
Description	Author affiliation: Computer Science and Engineering, United International University, Dhaka, Bangladesh (Khan, R.R.) \|\| Computer Science, Tokyo Institute of Technology, Japan (Sugiyama, M.)
Abstract	The goal of regression analysis is to estimate the conditional mean of an input-output relation. But if the data has multimodality, highly asymmetric distribution or heteroscedastic noise, then estimating the conditional mean is not sufficient. In these scenarios, the conditional distribution itself needs to be estimated. Recently a method called Least Squares Conditional Density Estimation (LSCDE) has been proposed for estimating conditional density. LSCDE estimates the conditional density by considering it as a ratio of two densities and directly estimating the ratio. This method works quite well but cannot make use of any available unlabeled samples. In this paper, the method LSCDE has been extended to semi-supervised settings so that the unlabeled samples can be used to improve accuracy of estimation.
Starting Page	109
Ending Page	112
File Size	543561
Page Count	4
File Format	PDF
ISBN	9781467314343
e-ISBN	9781467314367
DOI	10.1109/ICECE.2012.6471497
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-20
Publisher Place	Bangladesh
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Kernel Robots Semisupervised learning Regression analysis Joints Noise Least Squares Semi-supervised Learning Conditional Density
Content Type	Text
Resource Type	Article

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