Weighted Parzen Windows for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Programming Boosting via Column Generation
Machine Learning
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Parzen-Window Network Intrusion Detectors
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Column-generation boosting methods for mixture of kernels
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Mixture of Kernels via LPBoost Methods
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Applied Intelligence
Gaussian kernel optimization for pattern classification
Pattern Recognition
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Locality kernels for sequential data and their applications to parse ranking
Applied Intelligence
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Kernel machines such as Support Vector Machines (SVM) have exhibited successful performance in pattern classification problems mainly due to their exploitation of potentially nonlinear affinity structures of data through the kernel functions. Hence, selecting an appropriate kernel function, equivalently learning the kernel parameters accurately, has a crucial impact on the classification performance of the kernel machines. In this paper we consider the problem of learning a kernel matrix in a binary classification setup, where the hypothesis kernel family is represented as a convex hull of fixed basis kernels. While many existing approaches involve computationally intensive quadratic or semi-definite optimization, we propose novel kernel learning algorithms based on large margin estimation of Parzen window classifiers. The optimization is cast as instances of linear programming. This significantly reduces the complexity of the kernel learning compared to existing methods, while our large margin based formulation provides tight upper bounds on the generalization error. We empirically demonstrate that the new kernel learning methods maintain or improve the accuracy of the existing classification algorithms while significantly reducing the learning time on many real datasets in both supervised and semi-supervised settings.