数据采集与处理Issue(4)：703-713,11.DOI:10.16337/j.1004-9037.2015.04.001
面向频谱大数据处理的机器学习方法
Machine Learning Methods for Big Spectrum Data Processing
摘要
Abstract
With the rapid development of the mobile Internet and the Internet of Things ,the number of personal wireless devices has grown exponentially ,resulting in the increase of massive spectrum data . Therefore ,the big spectrum data are literally formed .Meanwhile ,the spectrum deficit is also increasing‐ly precarious .Effective big spectrum data processing is significant in improving the spectrum utilization . Firstly ,from a perspective of wireless communication ,a definition of big spectrum data is presented and its characteristics are also analyzed .Then ,promising machine learning methods to analyze and utilize the big spectrum data are summarized ,such as ,the distributed and parallel learning ,extreme learning ma‐chine ,kernel‐based learning ,deep learning ,reinforcement learning ,game learning ,and transfer learn‐ing .Finally ,several open issues and research trends are addressed .关键词
大数据/频谱大数据/机器学习/数据挖掘/无线通信/物联网Key words
big data/big spectrum data/machine learning/data mining/wireless communication/Inter-net of Things分类
信息技术与安全科学引用本文复制引用
吴启晖,邱俊飞,丁国如..面向频谱大数据处理的机器学习方法[J].数据采集与处理,2015,(4):703-713,11.基金项目
国家自然科学基金（61301160，61172062）资助项目。 （）
