Distributed Consensus Reduced Support Vector Machine for Binary Classification
李育杰教授國立交通大學 15:30-17:00 鴻經館M107Abstract：
Nowadays, Machine learning
performs astonishingly in many different fields. The more data we have, our
machine learning methods show better results. However, in some cases, the data owners
may not want to share the information they have, because those materials
contain privacy issues. On the other hand, sometimes we encounter a very large
dataset, which are difficult to store in a single machine. To deal with these
two problems, we propose the distributed consensus reduced support vector
machine (DCRSVM) for binary classification. Imagine that we have many local
working units and a central master, and each working unit owns its data. The
DCRSVM includes the following two merits. First, our method keeps the privacy
of data, so we are not going to disclose local data to the central master.
Besides, when we confront a large dataset, which is hard to store in a single
server, the central master can still derive a good machine learning model even
if the data stores only in local devices. Our method successfully solves the
problems we mentioned above, and it generates a competitive result.