||Prof. Hiroshi Mamitsuka
||Director of Bioinformtics Center, ICR, Kyoto Univeristy
||2019 年 6 月 20 日（周四）上午 10:00
A wide variety of machine learning techniques have been developed and used for a lot of applications. Traditionally data are vectors, meaning that each instance is a vector of features, by which the entire data set is a matrix. A typical example is a user-item matrix in e-commerce, where each instance is a user or item, for which features are purchase records. A recent, more emerging data type is a graph, particularly that with unique nodes, resulting in an adjacency matrix showing the similarity between nodes (instances). Examples are social networks, web links and biological networks, especially gene networks, in which edges represent biological relationships between nodes (genes), such as gene regulation, protein-protein interactions, and so on. In this talk, I will explain the key idea of machine learning for graphs (MLG) and also describe major problem settings and efficient algorithms under MLG.
Hiroshi Mamitsuka is a professor of Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan, being jointly appointed as a faculty of the Graduate School of Pharmaceutical Sciences of the same university. His current research interests lie in advancing machine learning techniques and applications of machine learning to a wide range of areas, including biological, medical, material and environmental sciences.