Query Learning Strategies Using Boosting and Bagging

TL;DR: This volume offers an overview of current efforts to deal with dataset and covariate shift, and places dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning.

TL;DR: This article reviews some research progress of weakly supervised learning, focusing on three typical types of weak supervision: incomplete supervision, where only a subset of training data is given with labels; inexact supervision, Where the training data are given with only coarse-grained labels; and inaccurate supervision,Where the given labels are not always ground-truth.

TL;DR: This paper surveys previously used query selection strategies for sequence models, and proposes several novel algorithms to address their shortcomings, and conducts a large-scale empirical comparison.

TL;DR: A brief overview of text classification algorithms is discussed in this article, where different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods are discussed, and the limitations of each technique and their application in real-world problems are discussed.