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Deep learning models for image classification of lymphoma: a pilot study in canine. [PDF]

J Vet Med Sci
Misaka R, Yoshida T, Tagawa M, Iwasaki R, Komatsu Y, Kayano M.   +5 more
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Collaborative representation and confidence-driven semi-supervised learning for hyperspectral image classification. [PDF]

Sci Rep
Chen Y, Lu H, Huang X.
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classification image

The Proceedings of the Annual Convention of the Japanese Psychological Association, 2006
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HSICNet a novel deep learning architecture for hyperspectral image classification in remote sensing and environmental monitoring. [PDF]

Sci Rep
Purnachand K, Samrin R, Guttikonda JB, Kotoju R, Addepalli PS, Mohan A.   +5 more
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Contextual image classification.

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i.Boosting for Image Classification

Multimedia and Expo, 2007 IEEE International Conference on, 2007
Traditional boosting method like adaboost, boosts a weak learning algorithm by updating the sample weights (the relative importance of the training samples) iteratively. In this paper, we propose to integrate feature re-weighting into boosting scheme, which not only weights the samples but also weights the feature elements iteratively.
Yijuan Lu, Tong Zhang 0007, Qi Tian 0001
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