In this thesis, we investigate a generic adaptation problem in multimedia and other areas, which is to adapt supervised classifiers trained from one or more source domains to a new classifier for a target domain that has only limited labeled examples. The foundation of our work is a general framework for function-level classifier adaptation based on the regularized loss minimization principle. Fundamentally different from existing adaptation techniques, this framework adapts a classifier by directly modifying its decision function rather than re-training over the data in source domains, making it highly efficient and applicable to any type of classifier. Under tins framework, one can derive concrete adaptation algorithms by plugging-in any loss and regularization functions, among which we elaborate on adaptive support vector machines (a-SVM) and adaptive kernel logistic regression (a-KLR). We further extend this framework for multi classifier adaptation, namely adapting multiple existing classifiers into a classifier for the target domain, in a way that the contributions of these existing classifiers are automatically determined. We evaluate the proposed approaches in cross-domain semantic concept detection based on TRECVID corpora. The results show that our approaches outperform existing (adaptation and non-adaptation) methods in terms of accuracy and/or efficiency, and adaptation Irons multiple classifiers offers further benefits. We also demonstrate the effectiveness of our approaches in adapting classifiers of teat documents and of EEG data. 
