The projectron: a bounded kernel-based Perceptron
Proceedings of the 25th international conference on Machine learning
Robust bounds for classification via selective sampling
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Identifying suspicious URLs: an application of large-scale online learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A simpler unified analysis of budget perceptrons
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Catching the drift: learning broad matches from clickthrough data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Polynomial to linear: efficient classification with conjunctive features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Tighter perceptron with improved dual use of cached data for model representation and validation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Bounded Kernel-Based Online Learning
The Journal of Machine Learning Research
λ-Perceptron: An adaptive classifier for data streams
Pattern Recognition
Towards context-aware personalization and a broad perspective on the semantics of news articles
Proceedings of the fourth ACM conference on Recommender systems
Kernel slicing: scalable online training with conjunctive features
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Example-dependent basis vector selection for kernel-based classifiers
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Learning to detect malicious URLs
ACM Transactions on Intelligent Systems and Technology (TIST)
Towards a context-sensitive online newspaper
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
Two one-pass algorithms for data stream classification using approximate MEBs
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Gas sensor drift mitigation using classifier ensembles
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Selective block minimization for faster convergence of limited memory large-scale linear models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Double Updating Online Learning
The Journal of Machine Learning Research
Preference-based policy learning
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
A kernel-based Perceptron with dynamic memory
Neural Networks
An online framework for learning novel concepts over multiple cues
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
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
A kernel fused perceptron for the online classification of large-scale data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
BDUOL: double updating online learning on a fixed budget
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Online Multiple Kernel Classification
Machine Learning
Online learning with multiple kernels: A review
Neural Computation
Fixed budget quantized kernel least-mean-square algorithm
Signal Processing
The Journal of Machine Learning Research
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The Perceptron algorithm, despite its simplicity, often performs well in online classification tasks. The Perceptron becomes especially effective when it is used in conjunction with kernel functions. However, a common difficulty encountered when implementing kernel-based online algorithms is the amount of memory required to store the online hypothesis, which may grow unboundedly as the algorithm progresses. Moreover, the running time of each online round grows linearly with the amount of memory used to store the hypothesis. In this paper, we present the Forgetron family of kernel-based online classification algorithms, which overcome this problem by restricting themselves to a predefined memory budget. We obtain different members of this family by modifying the kernel-based Perceptron in various ways. We also prove a unified mistake bound for all of the Forgetron algorithms. To our knowledge, this is the first online kernel-based learning paradigm which, on one hand, maintains a strict limit on the amount of memory it uses and, on the other hand, entertains a relative mistake bound. We conclude with experiments using real datasets, which underscore the merits of our approach.