Nonparametric Learning in High Dimensions

Nonparametric Learning in High Dimensions

This thesis develops flexible and principled nonparametric learning algorithms to explore, understand, and predict high dimensional and complex datasets. Such data appear frequently in modern scientific domains and lead to numerous important applications. For example, exploring high dimensional func...

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Bibliographic Details
Main Author: Liu, Han
Format: Others
Published: Research Showcase @ CMU 2010
Subjects:
machine learning
statistical inference
nonparametric methods
curse of dimensionality
regression
classification
multi-task learning
density estimation
undirected graphical models
structure learning
spatial-temporal adaptive learning
Online Access:http://repository.cmu.edu/dissertations/16
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1016&context=dissertations

Internet

http://repository.cmu.edu/dissertations/16
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1016&context=dissertations

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