Global optimization methods for designing and training neural networks Export

@inproceedings{citeulike:3384860, abstract = {This paper shows results of two approaches for the optimization of neural networks: one uses simulated annealing for optimizing both architectures and weights combined with backpropagation for fine tuning, while the other uses tabu search for the same purpose. Both approaches generate networks with good generalization performance (mean classification error of 1.68\% for simulated annealing and 0.64\% for tabu search) and low complexity (mean number of connections of 11.15 out of 36 for simulated annealing and 11.62 out of 36 for tabu search) for an odor recognition task in an artificial nose.}, author = {Yamazaki, A. and Ludermir, T. B. and de Souto, M. C. P.}, booktitle = {Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on}, citeulike-article-id = {3384860}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/SBRN.2002.1181455}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1181455}, doi = {10.1109/SBRN.2002.1181455}, journal = {Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on}, keywords = {connectionism-essay}, pages = {136--141}, posted-at = {2009-05-15 01:29:00}, priority = {2}, title = {Global optimization methods for designing and training neural networks}, url = {http://dx.doi.org/10.1109/SBRN.2002.1181455}, year = {2002} }

TY - CONF ID - citeulike:3384860 L3 - citeulike-article-id:3384860 N2 - This paper shows results of two approaches for the optimization of neural networks: one uses simulated annealing for optimizing both architectures and weights combined with backpropagation for fine tuning, while the other uses tabu search for the same purpose. Both approaches generate networks with good generalization performance (mean classification error of 1.68% for simulated annealing and 0.64% for tabu search) and low complexity (mean number of connections of 11.15 out of 36 for simulated annealing and 11.62 out of 36 for tabu search) for an odor recognition task in an artificial nose. JF - Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on EP - 141 TI - Global optimization methods for designing and training neural networks T2 - Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on SP - 136 KW - connectionism-essay AU - Yamazaki, A AU - Ludermir, TB AU - de Souto, MCP PY - 2002/// UR - http://dx.doi.org/10.1109/SBRN.2002.1181455 ER -