- Alexander Hauptmann, Jun Yang. 2009. A general framework for classifier adaptation and its applications in multimedia. Abstract: For the analysis and retrieval of multimedia data, machine learning techniques have been extensively applied to build models that map various feature vectors of the data into semantic labels. As multimedia data come from a wide variety of domains (e.g., genres, sources), each having its distinctive data characteristics, models trained from one domain do not usually generalize well to other domains. For example, the performance of semantic concept detectors trained from news video drops 60-70% when they are applied on documentary video. Meanwhile, it is prohibitively expensive to build new models for each and every domain due to the high cost for labeling training examples. Therefore, techniques for adapting models across different, domains are desirable for better performance and reduced human cost. 
