A Review on Temporal Reasoning Using Support Vector Machines

Abstract

Recently, Support Vector Machines have presented promissing results to various machine learning tasks, such as classification and regression. These good results have motivated its application to several complex problems, including temporal information analysis. In this context, some studies attempt to extract temporal features from data and submit these features in a vector representation to traditional Support Vector Machines. However, Support Vector Machines and its traditional variations do not consider temporal dependency among data. Thus, some approaches adapt Support Vector Machines internal mechanism in order to integrate some processing of temporal characteristics, attempting to make them able to interpret the temporal information inherent on data. This paper presents a review on studies covering this last approach for dealing with temporal information: incorporating temporal reasoning into Support Vector Machines and its variations.