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ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

April 2013, Vol. 25, No. 4, Pages 1107-1121
(doi: 10.1162/NECO_a_00421)
© 2013 Massachusetts Institute of Technology
Error Analysis of Coefficient-Based Regularized Algorithm for Density-Level Detection
Article PDF (138.03 KB)
Abstract

In this letter, we consider a density-level detection (DLD) problem by a coefficient-based classification framework with -regularizer and data-dependent hypothesis spaces. Although the data-dependent characteristic of the algorithm provides flexibility and adaptivity for DLD, it leads to difficulty in generalization error analysis. To overcome this difficulty, an error decomposition is introduced from an established classification framework. On the basis of this decomposition, the estimate of the learning rate is obtained by using Rademacher average and stepping-stone techniques. In particular, the estimate is independent of the capacity assumption used in the previous literature.