Abstract:

General machine learning assumes that the distribution of training data and test data are same, but the domain adaptation algorithms aims at handling different but similar distributions among training sets, which have a wide range of applications such as transfer learning, data mining, data correction, data projections. Support vector machine (SVM) attempts to find an optimal separating hyperplane for binaryclassification problems in highdimensional space, in order to ensure the minimum classification error rate. CCMEB proposed by I Tsang, as an improvement of the CVM, is particularly suitable for training on large datasets. In this article SVM and CCMEB are combined with probability distribution theory to formulate a novel domain adaptation approach (CCMEBSVMDA).  By calculating the center of each dataset, we can correct the dataset or identify the similarity of data between different domains.This fast algorithm has a good adaptability. As a validation we test it on the fields of “UCI data” and “text classification data” and the obtained experimental results indicate the effectiveness of the proposed algorithm.

Key words: SVM;domain adaptation;minimum enclosing ball;CCMEB