trNon-greedy active learning for text categorization using convex ansductive experimental design

Yu, Kai; Zhu, Shenghuo; Xu, Wei; Gong, Yi

doi:10.1145/1390334.1390442

Cited by 40 publications

(71 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first one is a subset of the Newsgroups corpus [20], which consists of 3970 documents with TFIDF features of 8014 dimensions. Each document belongs to exactly one of four categories: autos, motorcycles, baseball and hockey.…”

Section: Methodsmentioning

confidence: 99%

Discriminative Experimental Design

Zhang

Yeung

2011

Machine Learning and Knowledge Discovery in Databases

View full text Add to dashboard Cite

Abstract. Since labeling data is often both laborious and costly, the labeled data available in many applications is rather limited. Active learning is a learning approach which actively selects unlabeled data points to label as a way to alleviate the labeled data deficiency problem. In this paper, we extend a previous active learning method called transductive experimental design (TED) by proposing a new unlabeled data selection criterion. Our method, called discriminative experimental design (DED), incorporates both margin-based discriminative information and data distribution information and hence it can be seen as a discriminative extension of TED. We report experiments conducted on some benchmark data sets to demonstrate the effectiveness of DED.

show abstract

Section: Methodsmentioning

confidence: 99%

Discriminative Experimental Design

Zhang

Yeung

2011

Machine Learning and Knowledge Discovery in Databases

View full text Add to dashboard Cite

show abstract

“…Since our approach is motivated by recent progress in experimental design, we begin with a brief description of it. For details, please see [2,14,23,24].…”

Section: Related Workmentioning

confidence: 99%

“…The most popular active learning techniques include Support Vector Machine active learning (SVMactive) [19,20] and regression based active learning [2,14,23,24]. SVMactive asks the user to label those images which are closest to the SVM boundary.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Convex experimental design using manifold structure for image retrieval

Zhang

Chen

et al. 2009

Proceedings of the 17th ACM International Conference on Multimedia

View full text Add to dashboard Cite

Content Based Image Retrieval (CBIR) has become one of the most active research areas in computer science. Relevance feedback is often used in CBIR systems to bridge the semantic gap. Typically, users are asked to make relevance judgements on some query results, and the feedback information is then used to re-rank the images in the database. An effective relevance feedback algorithm must provide the users with the most informative images with respect to the ranking function. In this paper, we propose a novel active learning algorithm, called Convex Laplacian Regularized Ioptimal Design (CLapRID), for relevance feedback image retrieval. Our algorithm is based on a regression model which minimizes the least square error on the labeled images and simultaneously preserves the intrinsic geometrical structure of the image space. It selects the most informative images which minimize the average predictive variance. The optimization problem of CLapRID can be cast as a semidefinite programming (SDP) problem, and solved via interior-point methods. Experimental results on COREL database have demonstrate the effectiveness of the proposed algorithm for relevance feedback image retrieval.

show abstract

“…In such situations, once we have trained the classifier with the available training data, if we discover that its accuracy is insufficient we are left with the issue of how to further improve it, under the constraint that the amount of human effort available to perform additional labelling is limited. One solution is to apply active learning techniques (see, e.g., [Cohn et al 1994;Yu et al 2008]), which rank a set of unlabelled examples in terms of how useful they are expected to be, once manually labelled, for retraining a (hopefully) better classifier; this allows the human annotators to concentrate on the most promising examples This article is a substantially revised and extended version of a paper presented at the 2nd International Conference on the Theory of Information Retrieval (ICTIR'09). The order in which the authors are listed is alphabetical; each author has given an equally important contribution to this work.…”

Section: Introductionmentioning

confidence: 99%

Improving Text Classification Accuracy by Training Label Cleaning

Esuli

Sebastiani

2013

ACM Trans. Inf. Syst.

View full text Add to dashboard Cite

In text classification (TC) and other tasks involving supervised learning, labelled data may be scarce or expensive to obtain. Semisupervised learning and active learning are two strategies whose aim is maximizing the effectiveness of the resulting classifiers for a given amount of training effort. Both strategies have been actively investigated for TC in recent years. Much less research has been devoted to a third such strategy, training label cleaning (TLC), which consists in devising ranking functions that sort the original training examples in terms of how likely it is that the human annotator has mislabelled them. This provides a convenient means for the human annotator to revise the training set so as to improve its quality. Working in the context of boosting-based learning methods for multilabel classification we present three different techniques for performing TLC and, on three widely used TC benchmarks, evaluate them by their capability of spotting training documents that, for experimental reasons only, we have purposefully mislabelled. We also evaluate the degradation in classification effectiveness that these mislabelled texts bring about, and to what extent training label cleaning can prevent this degradation.

show abstract

trNon-greedy active learning for text categorization using convex ansductive experimental design

Cited by 40 publications

References 13 publications

Discriminative Experimental Design

Discriminative Experimental Design

Convex experimental design using manifold structure for image retrieval

Improving Text Classification Accuracy by Training Label Cleaning

Contact Info

Product

Resources

About