By Jiuwen Cao, Kezhi Mao, Erik Cambria, Zhihong Man, Kar-Ann Toh
This booklet includes a few chosen papers from the overseas convention on severe studying computing device 2014, which was once held in Singapore, December 8-10, 2014. This convention introduced jointly the researchers and practitioners of utmost studying computer (ELM) from quite a few fields to advertise study and improvement of “learning with out iterative tuning”. The publication covers theories, algorithms and purposes of ELM. It provides the readers a look of the newest advances of ELM.
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Example text
Is there a rigorous theoretical basis for the belief that an ELM would perform better with appropriately biased weights? 44 J. Tapson, P. de Chazal, and A. van Schaik In the work reported here we propose some answers to the first two points, by suggesting a fast closed-form expression for generation of weights biased towards the input training samples, and showing that in some important cases it consistently outperforms a conventional ELM. 3 Methodology In the method described here, which we refer to as Computed Input Weights ELM (CIW-ELM), we want to generate input weights which have the form of (3) or (7) above; that is to say, they must be (random) weighted sums of the training data samples.
Parallel Ensemble of Online Sequential Extreme Learning Machine train train (X0,k , T0,k ) valid valid (X0,k , T0,k ) valid valid , T0,k ) (X0,k OS-ELM0 T0test OS-ELMV0 alid V T esting Data o T test ggg T raining Data train train (X0,k , T0,k ) t e train train , Tm,k ) (Xm,k train train (Xm,k , Tm,k ) valid valid (Xm,k , Tm,k ) valid valid , Tm,k ) (Xm,k Bagging 33 Subspace Partitioning OS-ELMm test Tm OS-ELMVmalid T esting Data Sequential Learning Phase Cross Validation Testing Phase Voting Phase Fig.
The average testing RMSE of IR-ELM and EM-ELM on Servo and Concrete data sets Table 4. Totally IR-ELM performs better than EM-ELM for classification problems for incremental learning. From the figures we can see that IR-ELM 28 Z. Xu and M. Yao Table 5. 29 80 75 65 60 55 50 45 70 70 80 90 100 110 120 130 140 Hidden Nodes 150 160 170 40 20 180 25 (a) Vehicle data set 30 35 40 45 50 Hidden Nodes 55 60 65 70 (b) Glass data set Fig. 7. The average testing accuracy of IR-ELM and EM-ELM on Vehicle and Glass data sets 100 85 IR-ELM EM-ELM 80 95 Testing Accuracy(%) Testing Accuracy(%) IR-ELM EM-ELM 90 75 70 85 5 10 15 20 25 Hidden Nodes (a) Iris data set 30 35 40 65 10 15 20 25 30 35 Hidden Nodes 40 45 50 (b) Blood data set Fig.