演讲摘要：In the era of big data, there are abundant of data available across many different data sources in various formats. “Broad Learning” is a new type of learning task,
which focuses on fusing multiple large-scale information sources of diverse varieties together and carrying out synergistic data mining tasks across these fused
sources in one unified analytic. Great challenges exist on “Broad Learning” for the effective fusion of relevant knowledge across different data sources, which
depend upon not only the relatedness of these data sources, but also the target application problem. In this talk we examine how to fuse heterogeneous information
to improve mining effectiveness over various applications.