2005 Reports
Multi Facet Learning in Hilbert Spaces
We extend the kernel based learning framework to learning from linear functionals, such as partial derivatives. The learning problem is formulated as a generalized regularized risk minimization problem, possibly involving several different functionals. We show how to reduce this to conventional kernel based learning methods and explore a specific application in Computational Condensed Matter Physics.
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- cucs-054-05.pdf application/pdf 811 KB Download File
More About This Work
- Academic Units
- Computer Science
- Publisher
- Department of Computer Science, Columbia University
- Series
- Columbia University Computer Science Technical Reports, CUCS-054-05
- Published Here
- April 21, 2011