Least squares support vector machines for direction of arrival estimation with error control and validation.
Conference
·
OSTI ID:917153
- University of New Mexico, Albuquerque, NM
- University of New Mexico, Albuquerque, NM
The paper presents a multiclass, multilabel implementation of least squares support vector machines (LS-SVM) for direction of arrival (DOA) estimation in a CDMA system. For any estimation or classification system, the algorithm's capabilities and performance must be evaluated. Specifically, for classification algorithms, a high confidence level must exist along with a technique to tag misclassifications automatically. The presented learning algorithm includes error control and validation steps for generating statistics on the multiclass evaluation path and the signal subspace dimension. The error statistics provide a confidence level for the classification accuracy.
- Research Organization:
- Sandia National Laboratories
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 917153
- Report Number(s):
- SAND2003-0741C
- Country of Publication:
- United States
- Language:
- English
Similar Records
Approximate l-fold cross-validation with Least Squares SVM and Kernel Ridge Regression
An application of diophantine equations to the direction-of-arrival problem with phased interferometers
Approximate l-Fold Cross-Validation with Least Squares SVM and Kernel Ridge Regression
Conference
·
Mon Dec 31 23:00:00 EST 2012
·
OSTI ID:1111451
An application of diophantine equations to the direction-of-arrival problem with phased interferometers
Conference
·
Fri Dec 30 23:00:00 EST 1994
·
OSTI ID:482028
Approximate l-Fold Cross-Validation with Least Squares SVM and Kernel Ridge Regression
Conference
·
Thu Apr 10 00:00:00 EDT 2014
· 2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1
·
OSTI ID:1567338