Proceedings of ELM-2015 Volume 2: Theory, Algorithms and by Jiuwen Cao, Kezhi Mao, Jonathan Wu, Amaury Lendasse

By Jiuwen Cao, Kezhi Mao, Jonathan Wu, Amaury Lendasse

This booklet comprises a few chosen papers from the foreign convention on severe studying laptop 2015, which was once held in Hangzhou, China, December 15-17, 2015. This convention introduced jointly researchers and engineers to proportion and alternate R&D adventure on either theoretical reviews and functional purposes of the intense studying computer (ELM) procedure and mind learning.

This booklet covers theories, algorithms advert functions of ELM. It supplies readers a look of the newest advances of ELM.

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Wang et al. 78 76 74 72 70 SVM ELM PCELM 68 66 2 10 10 3 10 4 10 5 num. of features (b) The average training time of three classification algorithms. 200 180 160 time (s) 140 120 100 80 60 40 SVM ELM PCELM 20 0 10 2 103 104 105 num. of features 4 Conclusion In this paper, we have presented the SRP-based PC-ELM for scene classification. Experimental results on the benchmark dataset show that the PC-ELM network achieves a good balance between learning speed and generalization performance. Experimental results also show that PC-ELM has strong immunity to highdimensional feature space, which often results in over-fitting to other classifiers.

Conf. in Computer Vision (2007) 14. , and Lin, C-J. LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27. csie. tw/~cjlin/libsvm (2011) 15. : Image classifcation using random forests and ferns. In Proc, ICCV (2007) 16. , Fulkerson, B. M. Jonathan Wu Abstract The extreme learning machine (ELM), which was originally proposed for “generalized” single-hidden layer feedforward neural networks (SLFNs), provides efficient unified learning solutions for the applications of regression and classification.

The average results are obtained over 10 trials for all problems. For our proposed method, parameter C is selected from C ∈ {2−4 , … , 28 }. 1]. The parameter ???? equals 1 − ????. 1 All the testing accuracy is obtained following the same steps: first we use these methods to obtain data features, and then an SLFN classifier is used to generate testing accuracy. For DBN and SAE, we first reduce or increase the dimensions of 1 For DBN and SAE, it is impossible for us to set the parameter range too widely as the computational cost of these two methods is very high.

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