Nuclear norm regularized convolutional Max Pos@Top machine-Reference-Cited by-同舟云学术

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Nuclear norm regularized convolutional Max Pos@Top machine

Published:2016-11-18 Issue:2 Volume:30 Page:463-472
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Li Qinfeng,Zhou Xiaofeng,Gu Aihua,Li Zonghua,Liang Ru-Ze

Funder

Foundation of modern educational technology research of Jiangsu Province

The University Natural Science Foundation of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-016-2680-2/fulltext.html

Reference49 articles.

1. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M et al (2015) Tensorflow: large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org 1

2. Agarwal S (2011) The infinite push: a new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list. In: Proceedings of the 11th SIAM international conference on data mining, SDM 2011, pp 839–850

3. Al Madi, NS, Khan JI (2016) Measuring learning performance and cognitive activity during multimodal comprehension. In: 2016 7th international conference on information and communication systems (ICICS). IEEE, pp 50–55

4. Bewley A, Upcroft B (2016) From imagenet to mining: adapting visual object detection with minimal supervision. Springer Tracts Adv Robot 113:501–514

5. Boyd S, Cortes C, Mohri M, Radovanovic A (2012) Accuracy at the top. Adv Neural Inf Process Syst 2:953–961

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