Interactive Transfer Learning in Relational Domains

被引：6

作者：

Kumaraswamy, Raksha ^{[1
]}

Ramanan, Nandini ^{[2
]}

Odom, Phillip ^{[3
]}

Natarajan, Sriraam ^{[2
]}

机构：

[1] Univ Alberta, Comp Sci, Edmonton, AB, Canada

[2] Univ Texas Dallas, Comp Sci, Richardson, TX 75083 USA

[3] Georgia Inst Technol, Georgia Tech Res Inst, Atlanta, GA 30332 USA

来源：

KUNSTLICHE INTELLIGENZ | 2020年 / 34卷 / 02期

关键词：

22;

D O I：

10.1007/s13218-020-00659-6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We consider the problem of interactive transfer learning where a human expert provides guidance to the transfer learning algorithm that aims to transfer knowledge from a source task to a target task. One of the salient features of our approach is that we consider cross-domain transfer, i.e., transfer of knowledge across unrelated domains. We present an intuitive interface that allows for an expert to refine the knowledge in target task based on his/her expertise. Our results show that such guided transfer can effectively reduce the search space thus improving the efficiency and effectiveness of the transfer process.

引用

页码：181 / 192

页数：12

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