Support vector machines and learning about time

The analysis of temporal data is an important issue of current research, because most real-world data either explicitly or implicitly contains some information about time. The key to successfully solving temporal learning tasks is to analyze the assumptions that can be made and prior knowledge one has about the temporal process of the learning problem and find a representation of the data and a learning algorithm that makes effective use of this knowledge. This paper will present a concise overview of the application Support Vector Machines to different temporal learning tasks and the corresponding temporal representations.