DocumentCode :
3437750
Title :
Weighted Task Regularization for Multitask Learning
Author :
Yintao Liu ; Anqi Wu ; Dong Guo ; Ke-Thia Yao ; Raghavendra, Cauligi S.
Author_Institution :
Inf. Sci. Inst., Univ. of Southern California, Marina del Rey, CA, USA
fYear :
2013
fDate :
7-10 Dec. 2013
Firstpage :
399
Lastpage :
406
Abstract :
Multitask Learning has been proven to be more effective than the traditional single task learning on many real-world problems by simultaneously transferring knowledge among different tasks which may suffer from limited labeled data. However, in order to build a reliable multitask learning model, nontrivial effort to construct the relatedness between different tasks is critical. When the number of tasks is not large, the learning outcome may suffer if there exists outlier tasks that inappropriately bias majority. Rather than identifying or discarding such outlier tasks, we present a weighted regularized multitask learning framework based on regularized multitask learning, which uses statistical metrics, such as Kullback-Leibler divergence, to assign weights prior to regularization process that robustly reduces the impact of outlier tasks and results in better learned models for all tasks. We then show that this formulation can be solved using dual form like optimizing a standard support vector machine with varied kernels. We perform experiments using both synthetic dataset and real-world dataset from petroleum industry which shows that our methodology outperforms existing methods.
Keywords :
data handling; learning (artificial intelligence); support vector machines; Kullback-Leibler divergence; knowledge transfer; regularization process; statistical metrics; support vector machine; weighted regularized multitask learning framework; weighted task regularization; Accuracy; Educational institutions; Equations; Kernel; Optimization; Support vector machines; Training; anomaly detection; multitask learning; outlier task; svm; weighted regularization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4799-3143-9
Type :
conf
DOI :
10.1109/ICDMW.2013.158
Filename :
6753948
Link To Document :
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