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
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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
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
Prediction, Learning, and Games
Prediction, Learning, and Games
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
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
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
IEEE Transactions on Signal Processing
Multi kernel learning with online-batch optimization
The Journal of Machine Learning Research
Online feature selection for mining big data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Online Multiple Kernel Classification
Machine Learning
Online multi-modal distance learning for scalable multimedia retrieval
Proceedings of the sixth ACM international conference on Web search and data mining
Online learning with multiple kernels: A review
Neural Computation
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Online learning and kernel learning are two active research topics in machine learning. Although each of them has been studied extensively, there is a limited effort in addressing the intersecting research. In this paper, we introduce a new research problem, termed Online Multiple Kernel Learning (OMKL), that aims to learn a kernel based prediction function from a pool of predefined kernels in an online learning fashion. OMKL is generally more challenging than typical online learning because both the kernel classifiers and their linear combination weights must be learned simultaneously. In this work, we consider two setups for OMKL, i.e. combining binary predictions or real-valued outputs from multiple kernel classifiers, and we propose both deterministic and stochastic approaches in the two setups for OMKL. The deterministic approach updates all kernel classifiers for every misclassified example, while the stochastic approach randomly chooses a classifier(s) for updating according to some sampling strategies. Mistake bounds are derived for all the proposed OMKL algorithms.