Guest editor's introduction: special issue on inductive transfer learning

被引:20
作者
Silver, Daniel L. [1 ]
Bennett, Kristin P. [2 ]
机构
[1] Acadia Univ, Jodrey Sch Comp Sci, Wolfville, NS B4P 2R6, Canada
[2] Rensselaer Polytech Inst, Dept Math Sci, Troy, NY 12180 USA
关键词
Mach Learn; Lifelong Learning; Transfer Learning; Task Relatedness; Computational Learning Theory;
D O I
10.1007/s10994-008-5087-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Various problems related to inductive transfer or transfer learning submitted at the 2005 NIPS workshop, referring to the problem of retaining and applying the knowledge learned to develop efficiently an hypothesis for a new task, are considered. The paper 'Flexible latent variable models for multi-tasking learning' presents a Hierarchial Bayesian probabilistic framework for multi-task learning. 'Convex multi-task feature learning' introduces a method for learning sparse representations shared across multiple tasks. 'A notion of task relatedness yielding provable multiple-task learning guarantees' formalize one perspective on the nature of relatedness between tasks for multiple task learning. 'Transfer in variable-reward Hierarchical reinforcement learning' considers inductive transfer in the context of related reinforcement learning (RL) tasks.
引用
收藏
页码:215 / 220
页数:6
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