Please use this identifier to cite or link to this item:
|Title:||EXPLOITING DECENTRALIZED MULTI-AGENT COORDINATION FOR LARGE-SCALE MACHINE LEARNING PROBLEMS||Authors:||OUYANG RUOFEI||Keywords:||Multi-Agent System, Machine Learning, Gaussian process, Bayesian Optimization||Issue Date:||29-Jul-2016||Citation:||OUYANG RUOFEI (2016-07-29). EXPLOITING DECENTRALIZED MULTI-AGENT COORDINATION FOR LARGE-SCALE MACHINE LEARNING PROBLEMS. ScholarBank@NUS Repository.||Abstract:||Nowadays, the scale of machine learning problems becomes much larger than before. It raises a huge demand in distributed perception and distributed computation. A multi-agent system provides exceptional scalability for problems like active sensing and data fusion. However, many rich characteristics of large-scale machine learning problems have not been addressed yet such as large input domain, nonstationarity, and high dimensionality. This thesis identifes the challenges related to these characteristics from multi-agent perspective. By exploiting the correlation structure of data in large-scale problems, we propose multiagent coordination schemes that can improve the scalability of the machine learning models while preserving the computation accuracy.||URI:||http://scholarbank.nus.edu.sg/handle/10635/132189|
|Appears in Collections:||Ph.D Theses (Open)|
Show full item record
Files in This Item:
|thesis_1206.pdf||6.05 MB||Adobe PDF|
checked on May 23, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.