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- W2362123503 abstract "Cross-domain learning and classification involved in this paper attempts to effectively transfer the classification results obtained from supervised multisource domains to an unsupervised target domain. Generally speaking, although current cross-domain learning methods have obtained great successes for cross-single-domain learning problems, they will encounter overwhelming troubles in the sense of classification accuracy and running speed when carrying out them on cross-multisource datasets. In this paper, based on the logistic regression model and the proposed consensus measure,a multi-source cross-domain classification(MSCC) algorithm is proposed to realize effective cross-domain classification for the target domain. In order to enable the MSCC to work well for datasets, based on the algorithm CDdual(Dual coordinate descent method) as the recent advance about large-scale logistic regression, an MSCC s fast version MSCC-CDdual for datasets is derived and theoretically analysed. The experimental results on artificial data, text data and image data indicate that the proposed algorithm MSCC-CDdual has a fast speed, high classification accuracy and good domain adaption for cross-multisource datasets. The contributions of the work here contain three aspects:1) A novel consensus measure is proposed, which is suitable for boosting multi-classifiers and convenient for us to develop MSCC s fast version for datasets; 2) The proposed algorithm MSCC-CDdual is demonstrated to be suitable for cross-multisource learning for both small and datasets; 3) MSCC-CDdual exhibits its additional advantage, i.e., the applicability for high dimensional datasets from another large perspective." @default.
- W2362123503 created "2016-06-24" @default.
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- W2362123503 date "2014-01-01" @default.
- W2362123503 modified "2023-09-25" @default.
- W2362123503 title "A New Cross-multidomain Classification Algorithm and Its Fast Version for Large Datasets" @default.
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