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Large-scale text categorization by batch mode active learning
2006
Proceedings of the 15th international conference on World Wide Web - WWW '06
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Large-scale text categorization is an important research topic for Web data mining. ...
The key of the batch mode active learning is how to reduce the redundancy among the selected examples such that each example provides unique information for model updating. ...
Paul Komarek for sharing the text dataset and the logistic regression package, and comments from anonymous reviewers. ...
doi:10.1145/1135777.1135870
dblp:conf/www/HoiJL06
fatcat:y2nvxj23c5b35fkq6t47imq4ju
Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval
2009
IEEE Transactions on Knowledge and Data Engineering
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We apply our batch mode active learning framework to both text categorization and image retrieval. ...
In this paper, we present a framework for batch mode active learning, which selects a number of informative examples for manual labeling in each iteration. ...
ACKNOWLEDGMENTS The authors would like to thank Dr Paul Komarek for sharing some text data sets. The work was supported in part by the US National Science ...
doi:10.1109/tkde.2009.60
fatcat:a3k2amnstndc5lfde4xq5epft4
Batch-mode active learning for technology-assisted review
2015
2015 IEEE International Conference on Big Data (Big Data)
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Recent research demonstrates that Support Vector Machines (SVM) perform very well in finding a compact, yet effective, training dataset in an iterative fashion using batch-mode active learning. ...
This is fueled largely by dramatic growth in data volumes that may be associated with many matters and investigations. ...
It is also one of the best learning algorithms for large-scale text categorization. ...
doi:10.1109/bigdata.2015.7363867
dblp:conf/bigdataconf/SahaHBHJ15
fatcat:6rqn6nunuvhblaplhtdchlwmje
Batch Mode Active Learning for Networked Data
2012
ACM Transactions on Intelligent Systems and Technology
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We study a novel problem of batch mode active learning for networked data. ...
To scale to real large networks, we develop a parallel implementation of the algorithm. ...
This method has been applied to large scale text categorization [Hoi et al. 2006a ], medical image classification [Hoi et al. 2006b ] and image retrieval [Steven et al. 2009 ]. ...
doi:10.1145/2089094.2089109
fatcat:u7hrjucd6bfh5hv7ebznfzjrze
Analysing Predictive Coding Algorithms for Document Review
2021
International Journal for Research in Applied Science and Engineering Technology
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Keywords: Technology-assisted-review, predictive coding, machine learning, text classification, deep learning, CNN , Unscented Kalman Filter, Logistic Regression, SVM ...
Attorneys now have been using machine learning techniques like text classification to identify responsive information. ...
The superiority of their solution over existing methods (Brinker and SVMactive) for the experiment is supported by findings on a series of large-scale real-life legal document collections. ...
doi:10.22214/ijraset.2021.39076
fatcat:3yfyleh6trehjpfo3nakvn7sq4
Parallel MCMC Without Embarrassing Failures
[article]
2022
arXiv
pre-print
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Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesian inference to large datasets by using a two-step approach. ...
Our strategy, Parallel Active Inference (PAI), leverages Gaussian Process (GP) surrogate modeling and active learning. ...
We also acknowledge the computational resources provided by the Aalto Science-IT Project from Computer Science IT. ...
arXiv:2202.11154v2
fatcat:ssc5p6ox7fb4piq4talslnfuxa
A Survey of Active Learning for Text Classification using Deep Neural Networks
[article]
2020
arXiv
pre-print
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For active learning (AL) purposes, NNs are, however, less commonly used -- despite their current popularity. ...
By using the superior text classification performance of NNs for AL, we can either increase a model's performance using the same amount of data or reduce the data and therefore the required annotation ...
This research was partially funded by the Development Bank of Saxony (SAB) under project number 100335729. ...
arXiv:2008.07267v1
fatcat:joainuwblzbaplbls54tq4do3u
Combining link and content for collective active learning
2010
Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10
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In this paper, we study a novel problem Collective Active Learning, in which we aim to select a batch set of "informative" instances from a networking data set to query the user in order to improve the ...
accuracy of the learned classification model. ...
Experimental results show that our approach clearly outperforms (+6%) the baseline methods of single mode active learning and batch mode active learning on linked data sets. ...
doi:10.1145/1871437.1871740
dblp:conf/cikm/ShiZT10
fatcat:botdwfoozffttov2eleay4kfam
Batch mode Adaptive Multiple Instance Learning for computer vision tasks
2012
2012 IEEE Conference on Computer Vision and Pattern Recognition
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Such batch mode framework significantly accelerates the traditional MIL methods for large scale applications and can be also used in dynamic environments such as object tracking. ...
In this paper, we propose a novel batch mode framework, namely Batch mode Adaptive Multiple Instance Learning (BAMIL), to accelerate the instance-level MIL methods. ...
Acknowledgement This research is supported by the Singapore National Research Foundation under its Interactive & Digital Media (IDM) Public Sector R&D Funding Initiative and administered by the IDM Programme ...
doi:10.1109/cvpr.2012.6247949
dblp:conf/cvpr/LiDTX12
fatcat:5lfkmrwcxvee5pyylpofxi36be
Batch Mode Sparse Active Learning
2010
2010 IEEE International Conference on Data Mining Workshops
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BMSAL(Batch Mode Sparse Active Learning). ...
Keywords-batch mode sparse active learning; sparse classification; active learning; submodularity 2010 IEEE International Conference on Data Mining Workshops 978-0-7695-4257-7/10 $26.00 ...
We use two most popular batch mode active learning methods as our baseline: SVM active learning method is a batch mode active learning method. ...
doi:10.1109/icdmw.2010.175
dblp:conf/icdm/ShiZ10
fatcat:fuqc45ykgvd37bkptyf6ydomse
Possibilistic Fuzzy Clustering for Categorical Data Arrays Based on Frequency Prototypes and Dissimilarity Measures
2017
International Journal of Intelligent Systems and Applications
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Fuzzy clustering procedures for categorical data are proposed in the paper. ...
A detailed description of a possibilistic fuzzy clustering method based on frequency-based cluster prototypes and dissimilarity measures for categorical data is given. ...
to their batch-mode analogues, the most meaningful characteristic for experimental researching was considered a system's self-learning speed. ...
doi:10.5815/ijisa.2017.05.07
fatcat:wqnf674aofef7dn7lt6aafdix4
Batch Mode Active Learning for Multimedia Pattern Recognition
2012
2012 IEEE International Symposium on Multimedia
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This has expanded the possibility of solving real world problems using computational learning frameworks. ...
However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. ...
ACKNOWLEDGEMENTS My tenure at Arizona State University has been influenced and guided by a number of people to whom I am deeply indebted. ...
doi:10.1109/ism.2012.101
dblp:conf/ism/ChakrabortyBP12
fatcat:kvr4sjlulrcv5cdwtrapadskm4
An enhanced short text categorization model with deep abundant representation
2018
World wide web (Bussum)
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Many researches focus on data sparsity and ambiguity issues in short text categorization. ...
Short text categorization is a crucial issue to many applications, e.g., Information Retrieval, Question-Answering System, MRI Database Construction and so forth. ...
With large-scale embedding representation and topic model, we can extract useful latent semantic information for short text categorization. ...
doi:10.1007/s11280-018-0542-9
fatcat:4oyqoapdlfgbnmrbb7zrrxowkm
Malware Binary Image Classification Using Convolutional Neural Networks
2022
International Conference on Cyber Warfare and Security (ICIW)
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Furthermore, the proliferation of malicious files and new malware signatures increases year by year. ...
One of these cybersecurity tasks where machine learning may prove advantageous is malware analysis and classification. ...
Acknowledgement This material is based upon work supported by the National Science Foundation under Grant No. 1753900. ...
doi:10.34190/iccws.17.1.59
fatcat:3xgqmm3syfe3lninuqg5dxeumu
TARexp: A Python Framework for Technology-Assisted Review Experiments
[article]
2022
arXiv
pre-print
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Technology-assisted review (TAR) is an important industrial application of information retrieval (IR) and machine learning (ML). ...
Large scale, deterministically reproducible experiments are supported. ...
Expensive large scale experiments are therefore necessary to derive meaningful generalizations. ...
arXiv:2202.11827v1
fatcat:yzhxk6porfhhzo5drj4bwxiajy
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