The weighted majority algorithm
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A game of prediction with expert advice
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
The Relaxed Online Maximum Margin Algorithm
Machine Learning
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
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
Adaptive Online Prediction by Following the Perturbed Leader
The Journal of Machine Learning Research
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Prediction, Learning, and Games
Prediction, Learning, and Games
A DC-programming algorithm for kernel selection
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning low-rank kernel matrices
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning the unified kernel machines for classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Building Projectable Classifiers of Arbitrary Complexity
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Improved second-order bounds for prediction with expert advice
Machine Learning
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Tracking the best hyperplane with a simple budget Perceptron
Machine Learning
The Forgetron: A Kernel-Based Perceptron on a Budget
SIAM Journal on Computing
Training SVM with indefinite kernels
Proceedings of the 25th international conference on Machine learning
Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning
The projectron: a bounded kernel-based Perceptron
Proceedings of the 25th international conference on Machine learning
The weighted majority algorithm
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Learning kernels from indefinite similarities
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multiple indefinite kernel learning with mixed norm regularization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
SimpleNPKL: simple non-parametric kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Low-Rank Kernel Learning with Bregman Matrix Divergences
The Journal of Machine Learning Research
On multiple kernel learning with multiple labels
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Kernel combination versus classifier combination
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Online multiple kernel learning: algorithms and mistake bounds
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
A Family of Simple Non-Parametric Kernel Learning Algorithms
The Journal of Machine Learning Research
Double Updating Online Learning
The Journal of Machine Learning Research
IEEE Transactions on Signal Processing
Large scale online kernel classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Although both online learning and kernel learning have been studied extensively in machine learning, there is limited effort in addressing the intersecting research problems of these two important topics. As an attempt to fill the gap, we address a new research problem, termed Online Multiple Kernel Classification (OMKC), which learns a kernel-based prediction function by selecting a subset of predefined kernel functions in an online learning fashion. OMKC is in general more challenging than typical online learning because both the kernel classifiers and the subset of selected kernels are unknown, and more importantly the solutions to the kernel classifiers and their combination weights are correlated. The proposed algorithms are based on the fusion of two online learning algorithms, i.e., the Perceptron algorithm that learns a classifier for a given kernel, and the Hedge algorithm that combines classifiers by linear weights. We develop stochastic selection strategies that randomly select a subset of kernels for combination and model updating, thus improving the learning efficiency. Our empirical study with 15 data sets shows promising performance of the proposed algorithms for OMKC in both learning efficiency and prediction accuracy.