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A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
TLDR
This paper presents a general framework in which the structural learning problem can be formulated and analyzed theoretically, and relate it to learning with unlabeled data, and algorithms for structural learning will be proposed, and computational issues will be investigated. Expand
Solving large scale linear prediction problems using stochastic gradient descent algorithms
  • Tong Zhang
  • Mathematics, Computer Science
  • ICML
  • 4 July 2004
TLDR
Stochastic gradient descent algorithms on regularized forms of linear prediction methods, related to online algorithms such as perceptron, are studied, and numerical rate of convergence for such algorithms is obtained. Expand
Statistical behavior and consistency of classification methods based on convex risk minimization
We study how closely the optimal Bayes error rate can be approximately reached using a classification algorithm that computes a classifier by minimizing a convex upper bound of the classificationExpand
An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods
This book is an introduction to support vector machines and related kernel methods in supervised learning, whose task is to estimate an input-output functional relationship from a training set ofExpand
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
  • Tong Zhang
  • Mathematics, Computer Science
  • J. Mach. Learn. Res.
  • 1 December 2004
TLDR
It is shown that some risk minimization formulations can also be used to obtain conditional probability estimates for the underlying problem, which can be useful for statistical inferencing tasks beyond classification. Expand
Named Entity Recognition through Classifier Combination
This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classifiers (robust linear classifier, maximum entropy, transformation-basedExpand
Boosting with early stopping: Convergence and consistency
Boosting is one of the most significant advances in machine learning for classification and regression. In its original and computationally flexible version, boosting seeks to minimize empirically aExpand
Text Categorization Based on Regularized Linear Classification Methods
TLDR
A number of known linear classification methods as well as some variants in the framework of regularized linear systems are compared to discuss the statistical and numerical properties of these algorithms, with a focus on text categorization. Expand
A High-Performance Semi-Supervised Learning Method for Text Chunking
TLDR
A novel semi-supervised method that employs a learning paradigm which is to find "what good classifiers are like" by learning from thousands of automatically generated auxiliary classification problems on unlabeled data, which produces performance higher than the previous best results. Expand
The Value of Unlabeled Data for Classification Problems
TLDR
It is demonstrated that Fisher information matrices can be used to judge the asymp-totic value of unlabeled data and this methodology is applied to both passive partially supervised learning and active learning. Expand
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