Semi-supervised Subspace Learning and Application to Human Functional Magnetic 
Brain Resonance Imaging Data

Shelton, J

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Semi-supervised Subspace Learning and Application to Human Functional Magnetic Brain Resonance Imaging Data

Shelton, J. (2010). Semi-supervised Subspace Learning and Application to Human Functional Magnetic Brain Resonance Imaging Data. Master Thesis, Eberhard Karls Universität, Tübingen, Germany.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BF5E-9 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-AC9C-D

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Shelton, J^{1, 2}, Author

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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794

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Abstract: Machine learning is a modern and rapidly growing empirical science which integrates
themes in statistical inference and decision making with a focus on exploratory data analysis using computational methodology (Bishop (2006)). It is a branch of artificial intelligence which draws strongly upon methodology from linear algebra, optimization, and signal processing in order to develop and apply predictive models originating from statistics, computer science, and engineering. The main concern of machine learning is to design algorithms which enable machines to learn, particularly by finding patterns or regularities in data and using these patterns to drive some decision/analysis process (Bishop (2006); Duda et al. (2001); Schölkopf and Smola (2002)).

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Dates: Published in Print: 2010-07

Publication Status: Published in print

Pages: 92

Publishing info: Tübingen, Germany : Eberhard Karls Universität

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Identifiers: BibTex Citekey: 6758

Degree: Master

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