A topology synthesizing approach for classification of visual information

Dong, Le; Izquierdo, Ebroul

doi:10.1109/cbmi.2008.4564971

Cited by 1 publication

(1 citation statement)

References 40 publications

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“…In practice, the domain adaptive learning strategy has been successfully applied to real-time applications, such as multi-task clustering [3] ,WiFi localization [4], action recognition [5], sentiment classification [6], visual event recognition [7,8], object detection [9,10] and visual concept classification [11,12,13]. However, compared with non-learning methods [14,15], adaptive learning has more extensive applications.…”

Section: Introductionmentioning

confidence: 99%

Optimal kernel choice for domain adaption learning

Dong

Feng

Quan

et al. 2016

Engineering Applications of Artificial Intelligence

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In this paper, a kernel choice method is proposed for domain adaption, referred to as Optimal Kernel Choice Domain Adaption (OKCDA). It learns a robust classier and parameters associate with Multiple Kernel Learning side by side. Domain adaption kernel-based learning strategy has shown outstanding performance. It embeds two domains of different distributions, namely, the auxiliary and the target domains, into Hilbert Space, and exploits the labeled data from the source domain to train a robust kernel-based SVM classier for the target domain. We reduce the distributions mismatch by setting up a test statistic between the two domains based on the Maximum Mean Discrepancy (MMD) algorithm and minimize the Type II error, given an upper bound on error I. Simultaneously, we minimize the structural risk functional. In order to highlight the advantages of the proposed method, we tackle a text classication problem on 20 Newsgroups dataset and Email Spam dataset. The results demonstrate that our method exhibits outstanding performance.

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