Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging
Problems of Information Transmission
Worst-Case Analysis of Selective Sampling for Linear Classification
The Journal of Machine Learning Research
Noise Tolerant Variants of the Perceptron Algorithm
The Journal of Machine Learning Research
Applications of regularized least squares to pattern classification
Theoretical Computer Science
Tracking the best hyperplane with a simple budget Perceptron
Machine Learning
A primal-dual perspective of online learning algorithms
Machine Learning
Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning
Exploiting Cluster-Structure to Predict the Labeling of a Graph
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Bounded Kernel-Based Online Learning
The Journal of Machine Learning Research
Maximum Relative Margin and Data-Dependent Regularization
The Journal of Machine Learning Research
Virtual vector machine for Bayesian online classification
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
λ-Perceptron: An adaptive classifier for data streams
Pattern Recognition
Linear Algorithms for Online Multitask Classification
The Journal of Machine Learning Research
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
The Journal of Machine Learning Research
Re-adapting the regularization of weights for non-stationary regression
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Tracking the best hyperplane with a simple budget perceptron
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Distribution-aware online classifiers
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
An aggressive margin-based algorithm for incremental learning
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Hope and fear for discriminative training of statistical translation models
The Journal of Machine Learning Research
Confidence-weighted linear classification for text categorization
The Journal of Machine Learning Research
Adaptive two-view online learning for math topic classification
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Graph-Based transduction with confidence
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
An identity for kernel ridge regression
Theoretical Computer Science
Adaptive regularization of weight vectors
Machine Learning
Selective sampling on graphs for classification
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Selective sampling and active learning from single and multiple teachers
The Journal of Machine Learning Research
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Kernel-based linear-threshold algorithms, such as support vector machines and Perceptron-like algorithms, are among the best available techniques for solving pattern classification problems. In this paper, we describe an extension of the classical Perceptron algorithm, called second-order Perceptron, and analyze its performance within the mistake bound model of on-line learning. The bound achieved by our algorithm depends on the sensitivity to second-order data information and is the best known mistake bound for (efficient) kernel-based linear-threshold classifiers to date. This mistake bound, which strictly generalizes the well-known Perceptron bound, is expressed in terms of the eigenvalues of the empirical data correlation matrix and depends on a parameter controlling the sensitivity of the algorithm to the distribution of these eigenvalues. Since the optimal setting of this parameter is not known a priori, we also analyze two variants of the second-order Perceptron algorithm: one that adaptively sets the value of the parameter in terms of the number of mistakes made so far, and one that is parameterless, based on pseudoinverses.