IEEE Trans Neural Netw Learn Syst - Transfer learning for visual categorization: a survey.

Tópicos

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{ featur(1941) imag(1645) propos(1176) }
{ learn(2355) train(1041) set(1003) }
{ problem(2511) optim(1539) algorithm(950) }
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{ take(945) account(800) differ(722) }
{ data(1714) softwar(1251) tool(1186) }
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{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ concept(1167) ontolog(924) domain(897) }
{ control(1307) perform(991) simul(935) }
{ data(3963) clinic(1234) research(1004) }
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{ first(2504) two(1366) second(1323) }
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Resumo

Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition.

Resumo Limpo

regular machin learn data mine techniqu studi train data futur infer major assumpt futur data within featur space distribut train data howev due limit avail human label train data train data stay featur space distribut futur data guarante suffici enough avoid overfit problem realworld applic apart data target domain relat data differ domain can also includ expand avail prior knowledg target futur data transfer learn address crossdomain learn problem extract use inform data relat domain transfer use target task recent year transfer learn appli visual categor typic problem eg view diverg action recognit task concept drift imag classif task can effici solv paper survey stateoftheart transfer learn algorithm visual categor applic object recognit imag classif human action recognit

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