Local feature selection for multiple instance learning

Author:

Shahrjooihaghighi AliasgharORCID,Frigui Hichem

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Reference64 articles.

1. Amores, J. (2013). Multiple instance classification: review, taxonomy and comparative study. Artificial Intelligence, 201, 81–105.

2. Andrews, S., Tsochantaridis, I., & Hofmann, T. (2003). Support vector machines for multiple-instance learning. In Advances in neural information processing systems (pp. 577–584).

3. Ang, J.C, Mirzal, A., Haron, H., & Hamed, H.N.A. (2015). Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(5), 971–989.

4. Arai, H., Maung, C., Xu, K., & Schweitzer, H. (2016). Unsupervised feature selection by heuristic search with provable bounds on suboptimality. In Proceedings of the Thirtieth AAAI conference on artificial intelligence (pp. 666–672).

5. Archibald, R., & Fann, G. (2007). Feature selection and classification of hyperspectral images with support vector machines. IEEE Geoscience and Remote Sensing Letters, 4(4), 674–677.

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