Parzen windows for multi-class classification
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Related Research Unit(s)
|Journal / Publication||Journal of Complexity|
|Publication status||Published - Oct 2008|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-54849441621&origin=recordpage|
We consider the multi-class classification problem in learning theory. A learning algorithm by means of Parzen windows is introduced. Under some regularity conditions on the conditional probability for each class and some decay condition of the marginal distribution near the boundary of the input space, we derive learning rates in terms of the sample size, window width and the decay of the basic window. The choice of the window width follows from bounds for the sample error and approximation error. A novelly defined splitting function for the multi-class classification and a comparison theorem, bounding the excess misclassification error by the norm of the difference of function vectors, play an important role. © 2008 Elsevier Inc. All rights reserved.
- Approximation, Excess misclassification error, Multi-class classification, Parzen windows, Reproducing kernel Hilbert space