Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408065 | Neurocomputing | 2011 | 10 Pages |
Abstract
Incorporating prior knowledge (PK) into learning methods is an effective means to improve learning performance. The consistency and error theories of PK-based methods, which are of great theoretical importance, are still far from well established. Concentrating on the PK-based kernel regression, this paper proposes a methodology of analyzing the consistency and error. This methodology converts the specific methods firstly to a unified optimization problem and then to a unified solution expression, and a general consistency and error analysis tool is proposed and applied. A few examples are given to illustrate the analysis procedure.
Keywords
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Z. Sun, Z. Zhang, H. Wang,