Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Probabilistic Kernels for the Classification of Auto-Regressive Visual Processes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparative Study of Speaker Identification Methods: dPLRM, SVM and GMM
IEICE - Transactions on Information and Systems
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Co-occurring cluster mining for damage patterns analysis of a fuel cell
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
| Hi-index | 0.00 |
In the kernel method, appropriate selection of the kernel function is important for the construction of a high-performance classifier. The present paper describes a high-accuracy dynamic spectrum classification method using kernel classifiers with a divergence-based kernel. We introduce the divergence, which is a metric between two probability distributions, as a kernel function for similarity calculations of two dynamic spectra with appropriate statistical signal processing. The method is applied to two problems of acoustic signal classification: 1 identification of the condition of hazelnut shells using acoustic signals to maintain the quality and safety of the hazelnut product; 2 detection of worn-out banknotes by using acoustic signals to facilitate identification of counterfeit banknotes. In both applications, classification using the divergence-based kernel demonstrates better performance than classifications using popular kernels such as the Gaussian kernel or the polynomial kernel.