On efficient learning and classification kernel methods

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2012, Pages 2065-2068

  Kung, S Y ^a   Wu, Pei Yuan ^a  

^a PRINCETON UNIVERSITY   (United States)

Author keywords

anti support vectors; classification efficiency; intrinsic degree of kernels; learning efficiency; low power on line ECG detection; PDA; PPDA; SVM

Indexed keywords

CLASSIFICATION EFFICIENCY; INTRINSIC DEGREE OF KERNELS; LEARNING EFFICIENCY; LOW POWER; PDA; PPDA; SVM;

EFFICIENCY; ELECTROCARDIOGRAPHY; OPTIMIZATION; RADIAL BASIS FUNCTION NETWORKS; SIGNAL PROCESSING;

LEARNING SYSTEMS;

EID: 84867593296     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2012.6288316     Document Type: Conference Paper

References (5)

1. A. Vedaldi and A. Zisserman, "Efficient additive kernels via explicit feature maps," IEEE Trans. on PAMI, 2011.
2. S. Y. Kung, "Kernel approaches to unsupervised and supervised machine learning," Proc. PCM2009, pp. 1-32.
3. J. C. Platt, "Using analytic QP and sparseness to speed training of support vector machines," Advances in Neural Information Processing Systems, 1998.
4. PhysioNet, "http://www.physionet.org,".
5. K. H. Lee, S.Y. Kung, and N. Verma, "Kernel-energy trade-off in machine learning for implantable and wearable biomedical applications," ICASSP 2011.