Maximum margin classifier working in a set of strings.

ABSTRACT: Numbers and numerical vectors account for a large portion of data. However, recently, the amount of string data generated has increased dramatically. Consequently, classifying string data is a common problem in many fields. The most widely used approach to this problem is to convert strings into numerical vectors using string kernels and subsequently apply a support vector machine that works in a numerical vector space. However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws. In this study, we approach this classification problem by constructing a classi

SUBMITTER: Koyano H 

PROVIDER: S-EPMC4841474 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature