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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning.Expand
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On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notionExpand
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An Efficient Boosting Algorithm for Combining Preferences
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences arises in severalExpand
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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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Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use ofExpand
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Pegasos: primal estimated sub-gradient solver for SVM
We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number ofExpand
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Online Passive-Aggressive Algorithms
We present a unified view for online classification, regression, and uni-class problems. This view leads to a single algorithmic framework for the three problems. We prove worst case loss bounds forExpand
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Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin-based binary learningExpand
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BoosTexter: A Boosting-based System for Text Categorization
This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. WeExpand
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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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