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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
TLDR
This work describes and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal functions that can be chosen in hindsight. Expand
On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
TLDR
This paper describes the algorithmic implementation of multiclass kernel-based vector machines using a generalized notion of the margin to multiclass problems, and describes an efficient fixed-point algorithm for solving the reduced optimization problems and proves its convergence. Expand
An Efficient Boosting Algorithm for Combining Preferences
TLDR
This work describes and analyze an efficient algorithm called RankBoost for combining preferences based on the boosting approach to machine learning, and gives theoretical results describing the algorithm's behavior both on the training data, and on new test data not seen during training. Expand
Pegasos: primal estimated sub-gradient solver for SVM
TLDR
A simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines, which is particularly well suited for large text classification problems, and demonstrates an order-of-magnitude speedup over previous SVM learning methods. Expand
Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network
TLDR
A new part-of-speech tagger is presented that demonstrates the following ideas: explicit use of both preceding and following tag contexts via a dependency network representation, broad use of lexical features, and effective use of priors in conditional loglinear models. Expand
Improved Boosting Algorithms using Confidence-Rated Predictions
We describe several improvements to Freund and Schapire‘s AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give aExpand
Online Passive-Aggressive Algorithms
TLDR
This work presents a unified view for online classification, regression, and uni-class problems, and proves worst case loss bounds for various algorithms for both the realizable case and the non-realizable case. Expand
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
TLDR
A general method for combining the classifiers generated on the binary problems is proposed, and a general empirical multiclass loss bound is proved given the empirical loss of the individual binary learning algorithms. Expand
BoosTexter: A Boosting-based System for Text Categorization
TLDR
This work describes in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks, and presents results comparing the performance of Boos Texter and a number of other text-categorization algorithms on a variety of tasks. Expand
Improved Boosting Algorithms Using Confidence-rated Predictions
We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give aExpand
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