|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM200449699 |
| 003 |
DE-627 |
| 005 |
20231223220606.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231223s2010 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2009.188
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0668.xml
|
| 035 |
|
|
|a (DE-627)NLM200449699
|
| 035 |
|
|
|a (NLM)20724759
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Kim, JooSeuk
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a L2 kernel classification
|
| 264 |
|
1 |
|c 2010
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 29.11.2010
|
| 500 |
|
|
|a Date Revised 20.08.2010
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Nonparametric kernel methods are widely used and proven to be successful in many statistical learning problems. Well-known examples include the kernel density estimate (KDE) for density estimation and the support vector machine (SVM) for classification. We propose a kernel classifier that optimizes the L2 or integrated squared error (ISE) of a "difference of densities." We focus on the Gaussian kernel, although the method applies to other kernels suitable for density estimation. Like a support vector machine (SVM), the classifier is sparse and results from solving a quadratic program. We provide statistical performance guarantees for the proposed L2 kernel classifier in the form of a finite sample oracle inequality and strong consistency in the sense of both ISE and probability of error. A special case of our analysis applies to a previously introduced ISE-based method for kernel density estimation. For dimensionality greater than 15, the basic L2 kernel classifier performs poorly in practice. Thus, we extend the method through the introduction of a natural regularization parameter, which allows it to remain competitive with the SVM in high dimensions. Simulation results for both synthetic and real-world data are presented
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
|
| 700 |
1 |
|
|a Scott, Clayton D
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 32(2010), 10 vom: 01. Okt., Seite 1822-31
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
|
| 773 |
1 |
8 |
|g volume:32
|g year:2010
|g number:10
|g day:01
|g month:10
|g pages:1822-31
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2009.188
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 32
|j 2010
|e 10
|b 01
|c 10
|h 1822-31
|