Open AccessProceedings Article
Link-based Active Learning
Mustafa Bilgic,Lise Getoor +1 more
- 01 Jan 2009
TL;DR: Different ways of adapting existing active learning work to network data are proposed while utilizing links to select better examples to label, to improve the quality of the learned models.
read more
Abstract: Supervised and semi-supervised data mining techniques require labeled data. However, labeling examples is costly for many real-world applications. To address this problem, active learning techniques have been developed to guide the labeling process in an effort to minimize the amount of labeled data without sacrificing much from the quality of the learned models. Yet, most of the active learning methods to date have remained relatively agnostic to the rich structure offered by network data, often ignoring the relationships between the nodes of a network. On the other hand, the relational learning community has shown that the relationships can be very informative for various prediction tasks. In this paper, we propose different ways of adapting existing active learning work to network data while utilizing links to select better examples to label.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Learning Loss for Active Learning
Donggeun Yoo,In So Kweon +1 more
- 15 Jun 2019
TL;DR: In this article, the authors propose a novel active learning method that is simple but task-agnostic, and works efficiently with the deep networks, where a small parametric module, named ''loss prediction module'' to a target network, and learn it to predict target losses of unlabeled inputs.
•Posted Content
A Survey of Deep Active Learning
TL;DR: A formal classification method for the existing work in deep active learning is provided, along with a comprehensive and systematic overview, to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL.
794
State-Relabeling Adversarial Active Learning
Beichen Zhang,Liang Li,Shijie Yang,Shuhui Wang,Zheng-Jun Zha,Qingming Huang +5 more
- 14 Jun 2020
TL;DR: This paper proposes a state relabeling adversarial active learning model (SRAAL), that leverages both the annotation and the labeled/unlabeled state information for deriving the most informative unlabeled samples.
Contextual Diversity for Active Learning
Sharat Agarwal,Himanshu Arora,Saket Anand,Chetan Arora +3 more
- 23 Aug 2020
TL;DR: Sharat et al. as mentioned in this paper proposed Contextual Diversity (CD) to capture the confusion associated with spatially co-occurring classes and used the proposed CD measure within two AL frameworks: (1) a core-set based strategy and (2) a reinforcement learning based policy, for active frame selection.
107
•Posted Content
A critical look at the current train/test split in machine learning.
TL;DR: In this article, a new adaptive active learning architecture (AAL) is proposed, which involves an adaptive policy, in comparison with the traditional active learning that only unidirectionally adds data points to the training pool.
31
References
Active Learning Literature Survey
Burr Settles
- 01 Jan 2009
TL;DR: This report provides a general introduction to active learning and a survey of the literature, including a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date.
6.7K
•Proceedings Article
Semi-supervised learning using Gaussian fields and harmonic functions
Xiaojin Zhu,Zoubin Ghahramani,John Lafferty +2 more
- 21 Aug 2003
TL;DR: An approach to semi-supervised learning is proposed that is based on a Gaussian random field model, and methods to incorporate class priors and the predictions of classifiers obtained by supervised learning are discussed.
Collective Classification in Network Data
TL;DR: This article introduces four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.
Support vector machine active learning with applications to text classification
Simon Tong,Daphne Koller +1 more
TL;DR: Experimental results showing that employing the active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings are presented.
•Posted Content
A Sequential Algorithm for Training Text Classifiers
David D. Lewis,William A. Gale +1 more
TL;DR: An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task and reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.
2.7K
Related Papers (5)
Donggeun Yoo,In So Kweon +1 more
- 15 Jun 2019
Alex Krizhevsky
- 01 Jan 2009
David D. Lewis,Jason A. Catlett +1 more
- 10 Jul 1994