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Exploiting Task Relatedness for Mulitple Task Learning
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Exploiting Task Relatedness for Mulitple Task Learning   (Citations: 44)
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The approach of learning of multiple "related" tasks simultaneously has proven quite successful in practice; however, theoretical justification for this success has remained elusive. The starting point of previous work on multiple task learning has been that the tasks to be learnt jointly are somehow "algorithmically related", in the sense that the results of applying a specific learning algorithm to these tasks are assumed to be similar. We take a logical step backwards and offer a data...
Conference: Computational Learning Theory - COLT , pp. 567-580, 2003
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    • ...Learning multiple related tasks simultaneously by exploiting shared information across tasks has demonstrated advantages over those models learned within individual tasks [2, 4, 7, 14]...
    • ...X Y . Here,Pq is usually assumed dierent for each task but allPq’s are related, e.g., as discussed in [4]...

    Haiqin Yang, et al. Online learning for multi-task feature selection

    • ...The machine learning literature provides several attempts at formalizing the notion of interclass transfer (Ben-David and Schuller 2003; Thrun and Pratt 1997 ;F ink et al.2006)...

    Michael Fink, et al. From Aardvark to Zorro: A Benchmark for Mammal Image Classification

    • ...This idea is quite general and in the literature several such frameworks have been explored [2,3,4,5,6,7]...

    Jacob Abernethy, et al. Multitask Learning with Expert Advice

    • ...Multitask learning is one promising solution to this problem [60]‐[65], but the success of multitask learning largely depends on the relatedness of multiple learning tasks...
    • ...exactly be in the same place of the high-dimensional heterogeneous feature space [60]‐[65], thus such simple combinations may not be able to achieve a reliable ensemble classifier for the new task...

    Jianping Fan, et al. Incorporating Concept Ontology for Hierarchical Video Classification, ...

    • ...Theidentification ofType 1 scenarios relies on theability todecide whether twotasks are related andhow(e.g., [12], [13])...
    • ...2. Q -- allattributes ofS notinTtarget 3. Foreachattribute A dequeued fromQ 4. Foreachtraining instance IinS 5. Classify IusingTta,g,t 6. IfI'spredicted class isidentical toitstarget class label 7. Donothing 8. Else 9. Replace Ttarg,t's class nodewithanewnoderepresenting A 10. Addnewbranch tonodeA,labeled withA'svalue inI 11. Addleaf nodetonewbranch, labeled withI'starget class label 12. EndFor (1st pass) 13. ...

    Jun Won Lee, et al. Transfer Learning in Decision Trees

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Citations (44)

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