Text Categorization Based on Regularized Linear Classification Methods
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On the algorithmic implementation of multiclass kernel-based vector machines
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Large margin hierarchical classification
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Online multiclass learning by interclass hypothesis sharing
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Consistency of Trace Norm Minimization
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A convex formulation for learning shared structures from multiple tasks
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An accelerated gradient method for trace norm minimization
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Limitations of matrix completion via trace norm minimization
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Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning
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Exact matrix completion via convex optimization
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Regularization techniques for learning with matrices
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Max-margin embedding for multi-label learning
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This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using trace-norm regularization and study gradient-based optimization both for the linear case and the kernelized setting.