Accession Number : ADA303191
Title : Lifelong Learning: A Case Study.
Descriptive Note : Research rept.,
Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE
Personal Author(s) : Thrun, Sebastian
PDF Url : ADA303191
Report Date : NOV 1995
Pagination or Media Count : 39
Abstract : Machine learning has not yet succeeded in the design of robust learning algorithms that generalize well from very small datasets. In contrast, humans often generalize correctly from only a single training example, even if the number of potentially relevant features is large. To do so, they successfully exploit knowledge acquired in previous learning tasks, to bias subsequent learning. This paper investigates learning in a lifelong context. Lifelong learning addresses situations where a learner faces a stream of learning tasks. Such scenarios provide the opportunity for synergetic effects that arise if knowledge is transferred across multiple learning tasks. To study the utility of transfer, several approaches to lifelong learning are proposed and evaluated in an object recognition domain. It is shown that all these algorithms generalize consistently more accurately from scarce training data than comparable "single-task" approaches.
Descriptors : *LEARNING MACHINES, *LEARNING, ALGORITHMS, TRAINING, HUMANS, CASE STUDIES, RECOGNITION, TRANSFER, SYNERGISM, STREAMS.
Subject Categories : Psychology
Distribution Statement : APPROVED FOR PUBLIC RELEASE