Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
COLT '90 Proceedings of the third annual workshop on Computational learning theory
From on-line to batch learning
COLT '89 Proceedings of the second annual workshop on Computational learning theory
The perception: a probabilistic model for information storage and organization in the brain
Neurocomputing: foundations of research
The weighted majority algorithm
Information and Computation
Journal of Computer and System Sciences
Additive versus exponentiated gradient updates for linear prediction
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Machine Learning - Special issue on context sensitivity and concept drift
The robustness of the p-norm algorithms
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Regret bounds for prediction problems
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Relative Loss Bounds for Multidimensional Regression Problems
Machine Learning
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
General Convergence Results for Linear Discriminant Updates
Machine Learning
On-line Algorithms in Machine Learning
Developments from a June 1996 seminar on Online algorithms: the state of the art
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
The Robustness of the p-Norm Algorithms
Machine Learning
Online convex optimization in the bandit setting: gradient descent without a gradient
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
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 primal-dual perspective of online learning algorithms
Machine Learning
Data dependent concentration bounds for sequential prediction algorithms
COLT'05 Proceedings of the 18th annual conference on Learning Theory
On the generalization ability of on-line learning algorithms
IEEE Transactions on Information Theory
Regularization techniques for learning with matrices
The Journal of Machine Learning Research
Selective sampling on graphs for classification
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
Online learning is a well established learning paradigm which has both theoretical and practical appeals. The goal of online learning is to make a sequence of accurate predictions given knowledge of the correct answer to previous prediction tasks and possibly additional available information. Online learning has been studied in several research fields including game theory, information theory, and machine learning. It also became of great interest to practitioners due the recent emergence of large scale applications such as online advertisement placement and online web ranking. In this survey we provide a modern overview of online learning. Our goal is to give the reader a sense of some of the interesting ideas and in particular to underscore the centrality of convexity in deriving efficient online learning algorithms. We do not mean to be comprehensive but rather to give a high-level, rigorous yet easy to follow, survey.