Journals Proceedings

Abstract

Classification algorithms need labeled examples to train their model. However there are not enough labeled examples in some domains. There is an approach that training a classifier in one domain and use it to classify examples on different domains. This method is not always successful and this is called domain transfer problem. Spectral Feature Alignment is proposed as a solution to this problem [3]. In this study I investigate this algorithm with a demonstrative example and I do classification experiments on randomly created datasets. Support vector machines are used as a classification tool and the aim of the experiments is to find the impact of the spectral feature alignment on the classification accuracy. Based on the results of these experiments, I will discuss the possible research opportunities on this area.