Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Matrix computations (3rd ed.)
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
General convergence results for linear discriminant updates
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
The robustness of the p-norm algorithms
COLT '99 Proceedings of the twelfth 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
Lancelot: A FORTRAN Package for Large-Scale Nonlinear Optimization (Release A)
Lancelot: A FORTRAN Package for Large-Scale Nonlinear Optimization (Release A)
Two-Step Algorithms for Nonlinear Optimization with Structured Applications
SIAM Journal on Optimization
IEEE Transactions on Information Theory
Relative loss bounds for single neurons
IEEE Transactions on Neural Networks
Leave-one-out bounds for kernel methods
Neural Computation
An introduction to boosting and leveraging
Advanced lectures on machine learning
Covering number bounds of certain regularized linear function classes
The Journal of Machine Learning Research
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
Generalization error bounds for Bayesian mixture algorithms
The Journal of Machine Learning Research
Text categorization for a comprehensive time-dependent benchmark
Information Processing and Management: an International Journal
Focused named entity recognition using machine learning
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Multi-kernel regularized classifiers
Journal of Complexity
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
The Journal of Machine Learning Research
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
The Journal of Machine Learning Research
Reproducing kernel banach spaces for machine learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Reproducing Kernel Banach Spaces for Machine Learning
The Journal of Machine Learning Research
Computational Statistics & Data Analysis
Privacy protected knowledge management in services with emphasis on quality data
Proceedings of the 20th ACM international conference on Information and knowledge management
Large Linear Classification When Data Cannot Fit in Memory
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning convex combinations of continuously parameterized basic kernels
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Learning the coordinate gradients
Advances in Computational Mathematics
Regularized learning in Banach spaces as an optimization problem: representer theorems
Journal of Global Optimization
Vector-valued reproducing kernel Banach spaces with applications to multi-task learning
Journal of Complexity
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In this paper, we study a general formulation of linear prediction algorithms including a number of known methods as special cases. We describe a convex duality for this class of methods and propose numerical algorithms to solve the derived dual learning problem. We show that the dual formulation is closely related to online learning algorithms. Furthermore, by using this duality, we show that new learning methods can be obtained. Numerical examples will be given to illustrate various aspects of the newly proposed algorithms.