Big data are encountered in various areas, including Internet search, social networks, finance, business sectors, meteorology, genomics, complex physics simulations, biological and environmental research. Machine learning as an important tool of big data analytics is playing more and more important roles in the big data era. However, the characteristics of large volume, high velocity, variety and veracity bring challenges to current machine learning techniques. It is therefore desirable to discuss (1) how to scale up existing machine learning techniques for modeling and analyzing big data from various domains; (2) how to design new machine learning algorithms for various parallel/distributed machine learning platforms (such as Hadoop, GraphLab, Spark, etc.); and (3) how to design universal machine learning interfaces for GPUs or cloud computing architectures, and so on.
10月27日
2014
会议日期
注册截止日期
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