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Deterministic Neural Classification.
- Source :
-
Neural Computation . Jun2008, Vol. 20 Issue 6, p1565-1595. 31p. 7 Charts, 7 Graphs. - Publication Year :
- 2008
-
Abstract
- This letter presents a minimum classification error learning formulation for a single-layer feedforward network (SLFN). By approximating the nonlinear counting step function using a quadratic function, the classification error rate is shown to be deterministically solvable. Essentially the derived solution is related to an existing weighted least-squares method with class-specific weights set according to the size of data set. By considering the class-specific weights as adjustable parameters, the learning formulation extends the classification robustness of the SLFN without sacrificing its intrinsic advantage of being a closed-form algorithm. While the method is applicable to other linear formulations, our empirical results indicate SLFN's effectiveness on classification generalization. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08997667
- Volume :
- 20
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Neural Computation
- Publication Type :
- Academic Journal
- Accession number :
- 31738339
- Full Text :
- https://doi.org/10.1162/neco.2007.04-07-508