Fast exact multiplication by the Hessian
Neural Computation
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Machine Learning
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Parameter adaptation in stochastic optimization
On-line learning in neural networks
The robustness of the p-norm algorithms
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
The Relaxed Online Maximum Margin Algorithm
Machine Learning
Fast curvature matrix-vector products for second-order gradient descent
Neural Computation
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Acceleration Techniques for the Backpropagation Algorithm
Proceedings of the EURASIP Workshop 1990 on Neural Networks
On the Learnability and Design of Output Codes for Multiclass Problems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
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
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Exponential families for conditional random fields
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Iterative Kernel Principal Component Analysis for Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
Estimating the Support of a High-Dimensional Distribution
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Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Fast stochastic optimization for articulated structure tracking
Image and Vision Computing
A new perspective on an old perceptron algorithm
COLT'05 Proceedings of the 18th annual conference on Learning Theory
IEEE Transactions on Signal Processing
Limited stochastic meta-descent for kernel-based online learning
Neural Computation
Preferential text classification: learning algorithms and evaluation measures
Information Retrieval
Tracking a moving hypothesis for visual data with explicit switch detection
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Image Denoising with Kernels Based on Natural Image Relations
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Online SVR Training by Solving the Primal Optimization Problem
Journal of Signal Processing Systems
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This paper presents an online support vector machine (SVM) that uses the stochastic meta-descent (SMD) algorithm to adapt its step size automatically. We formulate the online learning problem as a stochastic gradient descent in reproducing kernel Hilbert space (RKHS) and translate SMD to the nonparametric setting, where its gradient trace parameter is no longer a coefficient vector but an element of the RKHS. We derive efficient updates that allow us to perform the step size adaptation in linear time. We apply the online SVM framework to a variety of loss functions, and in particular show how to handle structured output spaces and achieve efficient online multiclass classification. Experiments show that our algorithm outperforms more primitive methods for setting the gradient step size.