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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
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
A theory of learning from different domains
TLDR
A classifier-induced divergence measure that can be estimated from finite, unlabeled samples from the domains and shows how to choose the optimal combination of source and target error as a function of the divergence, the sample sizes of both domains, and the complexity of the hypothesis class. Expand
Online Large-Margin Training of Dependency Parsers
TLDR
An effective training algorithm for linearly-scored dependency parsers that implements online large-margin multi-class training on top of efficient parsing techniques for dependency trees is presented. Expand
Analysis of Representations for Domain Adaptation
TLDR
The theory illustrates the tradeoffs inherent in designing a representation for domain adaptation and gives a new justification for a recently proposed model which explicitly minimizes the difference between the source and target domains, while at the same time maximizing the margin of the training set. Expand
On the Learnability and Design of Output Codes for Multiclass Problems
TLDR
This paper discusses for the first time the problem of designing output codes for multiclass problems, and gives a time and space efficient algorithm for solving the quadratic program. Expand
Adaptive regularization of weight vectors
TLDR
Empirical evaluations show that AROW achieves state-of-the-art performance on a wide range of binary and multiclass tasks, as well as robustness in the face of non-separable data. Expand
Ultraconservative online algorithms for multiclass problems
In this paper we study a paradigm to generalize online classification algorithms for binary classification problems to multiclass problems. The particular hypotheses we investigate maintain oneExpand
Pranking with Ranking
TLDR
A simple and efficient online algorithm is described, its performance in the mistake bound model is analyzed, its correctness is proved, and it outperforms online algorithms for regression and classification applied to ranking. Expand
Confidence-weighted linear classification
TLDR
Empirical evaluation on a range of NLP tasks show that the confidence-weighted linear classifiers introduced here improves over other state of the art online and batch methods, learns faster in the online setting, and lends itself to better classifier combination after parallel training. Expand
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