Machine Learning
SIAM Review
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Knowledge Representation in Fuzzy Logic
IEEE Transactions on Knowledge and Data Engineering
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
MARK: a boosting algorithm for heterogeneous kernel models
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Face Recognition by Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A New Fuzzy Support Vector Machine Based on the Weighted Margin
Neural Processing Letters
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
The Image Processing Handbook, Fifth Edition (Image Processing Handbook)
The Image Processing Handbook, Fifth Edition (Image Processing Handbook)
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A neural network-based model for paper currency recognition and verification
IEEE Transactions on Neural Networks
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
The evidence framework applied to support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Posterior probability support vector Machines for unbalanced data
IEEE Transactions on Neural Networks
Efficient hyperkernel learning using second-order cone programming
IEEE Transactions on Neural Networks
Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines
IEEE Transactions on Neural Networks
Measuring financial risk with generalized asymmetric least squares regression
Applied Soft Computing
An efficient multiple-kernel learning for pattern classification
Expert Systems with Applications: An International Journal
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Finding an efficient method to detect counterfeit banknotes is an imperative task in business transactions. In this paper, we propose a system based on multiple-kernel support vector machines for counterfeit banknote recognition. A support vector machine (SVM) to minimize false rates is developed. Each banknote is divided into partitions and the luminance histograms of the partitions are taken as the input of the system. Each partition is associated with its own kernels. Linearly weighted combination is adopted to combine multiple kernels into a combined matrix. Optimal weights with kernel matrices in the combination are obtained through semi-definite programming (SDP) learning. Two strategies are adopted to reduce the amount of time and space required by the SDP method. One strategy assumes the non-negativity of the kernel weights, and the other one is to set the sum of the weights to be unity. Experiments with Taiwanese banknotes show that the proposed approach outperforms single-kernel SVMs, standard SVMs with SDP, and multiple-SVM classifiers.