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The leave-one-out cross-validation is an important parameter selection strategy for SVM-like family, including SVM and SVR. However, due to the high computational complexity, the adaptability of this strategy is restricted. In this paper, aiming at its practical application, a fast leave-one-out cross-validation method by using an adjustment factor is proposed which focusses especially on the practicability for the SVM-like family where the decision function can be expressed by explicit dot-product of each training sample pair. The ability of the proposed method in fast parameter selection is better than that of original leave-one-out cross-validation with the same or comparable learning performance. The simulation results indicate the effectiveness and speedup of the proposed leave-one-out cross-validation method. [ABSTRACT FROM AUTHOR]

Multiple source transfer learning (MSTL) has been obtaining more and more applications especially from several related source domains to help the learning task on target domain. However, multiple source transfer learning algorithms often deal with the corresponding quadratic programming problems which may suffer a big computational burden caused by the kernel matrix computation. In this paper, a novel common-decision-vector based multiple source transfer classification learning (CDV-MSTL) is proposed which doesn't depend on the intrinsic structure of data. This algorithm is based on the structural risk minimization principle and the SVM like framework, so it has good adaptability and better accuracy. Based on the theory of CVM, CDV-MSTL is extended to its CVM based version which can realize fast training for large scale data. Extensive experiments on synthetic and real-world datasets demonstrate the significant improvement in classification performance obtained by the proposed algorithm over existing MSTL algorithm. [ABSTRACT FROM AUTHOR]