Collaborative online multitask learning
We study the problem of online multitask learning for solving multiple related classification tasks in parallel, aiming at classifying every sequence of data received by each task accurately and efficiently. One practical example of online multitask learning is the micro-blog sentiment detection on...
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sg-smu-ink.sis_research-32792020-04-02T05:23:22Z Collaborative online multitask learning LI, Guangxia HOI, Steven C. H. CHANG, Kuiyu LIU, Wenting JAIN, Ramesh We study the problem of online multitask learning for solving multiple related classification tasks in parallel, aiming at classifying every sequence of data received by each task accurately and efficiently. One practical example of online multitask learning is the micro-blog sentiment detection on a group of users, which classifies micro-blog posts generated by each user into emotional or non-emotional categories. This particular online learning task is challenging for a number of reasons. First of all, to meet the critical requirements of online applications, a highly efficient and scalable classification solution that can make immediate predictions with low learning cost is needed. This requirement leaves conventional batch learning algorithms out of consideration. Second, classical classification methods, be it batch or online, often encounter a dilemma when applied to a group of tasks, i.e., on one hand, a single classification model trained on the entire collection of data from all tasks may fail to capture characteristics of individual task; on the other hand, a model trained independently on individual tasks may suffer from insufficient training data. To overcome these challenges, in this paper, we propose a collaborative online multitask learning method, which learns a global model over the entire data of all tasks. At the same time, individual models for multiple related tasks are jointly inferred by leveraging the global model through a collaborative online learning approach. We illustrate the efficacy of the proposed technique on a synthetic dataset. We also evaluate it on three real-life problems-spam email filtering, bioinformatics data classification, and micro-blog sentiment detection. Experimental results show that our method is effective and scalable at the online classification of multiple related tasks 2014-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2279 info:doi/10.1109/TKDE.2013.139 https://ink.library.smu.edu.sg/context/sis_research/article/3279/viewcontent/CollaborativeOnline_MultitaskLearning_2014_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial intelligence Data mining Machine learning classification learning systems multitask learning online learning Computer Sciences Databases and Information Systems |
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Artificial intelligence Data mining Machine learning classification learning systems multitask learning online learning Computer Sciences Databases and Information Systems LI, Guangxia HOI, Steven C. H. CHANG, Kuiyu LIU, Wenting JAIN, Ramesh Collaborative online multitask learning |
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We study the problem of online multitask learning for solving multiple related classification tasks in parallel, aiming at classifying every sequence of data received by each task accurately and efficiently. One practical example of online multitask learning is the micro-blog sentiment detection on a group of users, which classifies micro-blog posts generated by each user into emotional or non-emotional categories. This particular online learning task is challenging for a number of reasons. First of all, to meet the critical requirements of online applications, a highly efficient and scalable classification solution that can make immediate predictions with low learning cost is needed. This requirement leaves conventional batch learning algorithms out of consideration. Second, classical classification methods, be it batch or online, often encounter a dilemma when applied to a group of tasks, i.e., on one hand, a single classification model trained on the entire collection of data from all tasks may fail to capture characteristics of individual task; on the other hand, a model trained independently on individual tasks may suffer from insufficient training data. To overcome these challenges, in this paper, we propose a collaborative online multitask learning method, which learns a global model over the entire data of all tasks. At the same time, individual models for multiple related tasks are jointly inferred by leveraging the global model through a collaborative online learning approach. We illustrate the efficacy of the proposed technique on a synthetic dataset. We also evaluate it on three real-life problems-spam email filtering, bioinformatics data classification, and micro-blog sentiment detection. Experimental results show that our method is effective and scalable at the online classification of multiple related tasks |
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LI, Guangxia HOI, Steven C. H. CHANG, Kuiyu LIU, Wenting JAIN, Ramesh |
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LI, Guangxia HOI, Steven C. H. CHANG, Kuiyu LIU, Wenting JAIN, Ramesh |
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LI, Guangxia |
title |
Collaborative online multitask learning |
title_short |
Collaborative online multitask learning |
title_full |
Collaborative online multitask learning |
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Collaborative online multitask learning |
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Collaborative online multitask learning |
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collaborative online multitask learning |
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Institutional Knowledge at Singapore Management University |
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2014 |
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https://ink.library.smu.edu.sg/sis_research/2279 https://ink.library.smu.edu.sg/context/sis_research/article/3279/viewcontent/CollaborativeOnline_MultitaskLearning_2014_afv.pdf |
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