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Radial Basis Functions: An Algebraic Approach (with Data Mining Applications) (English)
| Description: |
September 20, 2004
Radial Basis Functions (RBF) have now become a very popular tool, both for classification and prediction tasks. The recent flurry of research in Support Vector Machines(SVM) has provided further impetus to their growth. Yet, most algorithms for their design are basically iterative and lead to irreproducible results. The authors of this tutorial have been working on an innovative new approach for the design and evaluation of radial basis function models. Our algorithm is based on purely algebraic considerations, is non-iterative, and yields reproducible designs .These features are completely unique to our new approach. We have employed this modeling methodology for many sets of problems in software engineering, microarray data analysis, and, recently, for clustering applications in bioinformatics. The purpose of this tutorial is to present the mathematical underpinning of this approach, describe its algorithmic details, and discuss selected datamining applications. Also, we want to briefly discuss how it compares and contrasts with the current SVM work. A brief outline of the proposed tutorial follows:
- Radial Basis Function model
- Algebraic preliminaries (singular value decomposition; QR factorization, etc.)
- Mathematical underpinnings of the new methodology
- Algorithmic details
- Classification and prediction formulations
- Datamining applications in software engineering, and bioinformatics
- Comparative assessment and current focus.
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| Lecturer: |
Goel, Amrit L.
Shin, Miyoung
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| Language: |
English |
| URL: |
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| Matrial: |
T4.pdf (1067 KB) |
| Date: |
2004
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| Address: |
ECML/PKDD2004
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