Download Artificial Neural Networks in Pattern Recognition: 6th IAPR by Neamat El Gayar, Friedhelm Schwenker, Cheng Suen PDF
By Neamat El Gayar, Friedhelm Schwenker, Cheng Suen
This e-book constitutes the refereed court cases of the sixth IAPR TC3 overseas Workshop on man made Neural Networks in trend reputation, ANNPR 2014, held in Montreal, quality control, Canada, in October 2014. The 24 revised complete papers offered have been rigorously reviewed and chosen from 37 submissions for inclusion during this quantity. They hide a wide range of issues within the box of studying algorithms and architectures and discussing the newest study, effects, and concepts in those areas.
Read or Download Artificial Neural Networks in Pattern Recognition: 6th IAPR TC 3 International Workshop, ANNPR 2014, Montreal, QC, Canada, October 6-8, 2014. Proceedings PDF
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Additional resources for Artificial Neural Networks in Pattern Recognition: 6th IAPR TC 3 International Workshop, ANNPR 2014, Montreal, QC, Canada, October 6-8, 2014. Proceedings
Comparing the results for the MM and MAE criteria, there is not much diﬀerence of the recognition rates of the test data sets (statistically comparable). , the breast cancer (3) and hepatocellular carcinoma data sets), 44 S. Abe Table 2. Performance comparison of incremental BABD and batch BABD Data (Tr/Te/In) B. 0709) B. 0729) B. 0474) B. 0816) C. 0880) H. 0708) H. 0392) P. 6 9157±3141 0/17/1 18/0/0 0/1/17 Incremental Feature Selection by BABD Using LS SVRs 45 the MAE criterion selected more features and thus feature selection time was longer.
0709) B. 0729) B. 0474) B. 0816) C. 0880) H. 0708) H. 0392) P. 6 9157±3141 0/17/1 18/0/0 0/1/17 Incremental Feature Selection by BABD Using LS SVRs 45 the MAE criterion selected more features and thus feature selection time was longer. But for the colon cancer data sets, the opposite was true. The above results conﬁrm that the MAE criterion is comparable to the MM criterion. From the “Summary” rows, we found that multi-pass incremental BABD showed the best recognition rates for the test data sets, but the numbers of selected features were the largest and also feature selection was slowest.
Test results indicate that the proposed method managed to ﬁnd the optimal number of hidden nodes for both the XOR and 3-parity problems which fully decorrelates the hidden layer outputs. : Dynamic node creation in backpropagation networks. : The cascade-correlation learning architecture. In: Advances in Neural Information Processing Systems 2, pp. 524–532. : Constructive algorithms for structure learning in feedforward neural networks for regression problems. : A resource-allocating network for function interpolation.