| Paper: | MLSP-L3.1 | ||
| Session: | Learning Theory and Modeling | ||
| Time: | Friday, May 21, 15:30 - 15:50 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Learning Theory and Modeling | ||
| Title: | THEORY OF MONTE CARLO SAMPLING-BASED ALOPEX ALGORITHMS FOR NEURAL NETWORKS | ||
| Authors: | Zhe Chen; McMaster University | ||
| Simon Haykin; McMaster University | |||
| Suzanna Becker; McMaster University | |||
| Abstract: | We propose two novel Monte Carlo sampling-based Alopex algorithmsfor training neural networks. The proposed algorithms naturallycombine the sequential Monte Carlo estimation and Alopex-likeprocedure for gradient-free optimization, and the learningproceeds within the recursive Bayesian estimation framework.Experimental results on various problems show encouragingconvergence results. | ||
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