التفاصيل البيبلوغرافية
| العنوان: |
Stable and Efficient Gaussian Process Calculations. |
| المؤلفون: |
Foster, Leslie1 FOSTER@MATH.SJSU.EDU, Waagen, Alex1 AWAAGEN@MAILBOLT.COM, Aijaz, Nabeela1 NABBOA@YAHOO.COM, Hurley, Michael1 MHURLEY@GMAIL.COM, Luis, Apolonio1 JPOLOROLU@GMAIL.COM, Rinsky, Joel1 JOELRINSKY@YAHOO.COM, Satyavolu, Chandrika1 CHANDRIKAS84@YAHOO.COM, Way, Michael J.2 M ICHAEL.J.WAY@NASA.GOV, Gazis, Paul3 PGAZIS@MAIL.ARC.NASA.GOV, Srivastava, Ashok3 ASHOK@EMAIL.ARC.NASA.GOV |
| المصدر: |
Journal of Machine Learning Research. 4/1/2009, Vol. 10 Issue 4, p857-882. 26p. 1 Diagram, 4 Charts, 4 Graphs. |
| مصطلحات موضوعية: |
*APPROXIMATION theory, *LINEAR statistical models, GAUSSIAN processes, SUPERVISED learning, MACHINE learning |
| مستخلص: |
The use of Gaussian processes can be an effective approach to prediction in a supervised learning environment. For large data sets, the standard Gaussian process approach requires solving very large systems of linear equations and approximations are required for the calculations to be practical. We will focus on the subset of regressors approximation technique. We will demonstrate that there can be numerical instabilities in a well known implementation of the technique. We discuss alternate implementations that have better numerical stability properties and can lead to better predictions. Our results will be illustrated by looking at an application involving prediction of galaxy redshift from broadband spectrum data. [ABSTRACT FROM AUTHOR] |
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