Transfer Learning Algorithms and Applications

Transfer Learning: Algorithms and Applications presents an in-depth discussion on practices for transfer learning, exploring emerging fields that includes a theoretical analysis of various algorithms and problems that lay a solid foundation for future advances in the field.

In the era of Big Data, machine learning methods are widely used in natural language processing, computer vision, speech, and in signal processing communities. However, the current standard machine learning techniques, such as supervised classifiers, tend to fail when the data distribution and/or structure changes over training and test settings.

Current techniques addressing machine learning problems can only address a few isolated tasks at one time. Transfer learning, adapted from how humans learn, models the distribution and structure difference between training and test settings.

• Introduces transfer learning with a systematic approach, discussing theory and providing applications, including but not limited to, image classification, natural language techniques, medicine, and web search ranking techniques
• Provides a state-of-the-art overview of the most recent developments in transfer learning, including unsupervised, supervised, and semi-supervised transfer learning, multitask learning, domain similarity estimation, and the applications of transfer learning
• Presents relevant algorithms with detailed discussions, including background, derivation, and comparisons
• Discusses extensive experimental results using real application datasets to demonstrate the performance of various algorithms

Dr. Makoto Yamada is a research scientist at Yahoo Labs. His research interests include machine learning and its application to natural language processing, signal processing, and computer vision. He has published numerous research articles in top venues within these fields, including the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), International Journal of Computer Vision (IJCV), Machine Learning Journal (MLJ), Neural Computation (NECO), NIPS, ICML, AISTATS, AAAI, IJCAI, CIKM, ICDM, and ECCV.
Dr. Jianhui Chen is a research scientist in Yahoo Labs. His research interests include multi-task learning, kernel learning, dimension reduction, and stochastic optimization. He has published research papers in top machine learning/data mining venues, including ICML, NIPS, AISTATS, IJCAI, KDD, TPAMI, JMLR, and TKDD. He also serves as PC members/reviewers for multiple top conferences/journals in relevant fields.
Dr. Yi Chang is director of sciences in Yahoo Labs, where he leads the search and anti-abuse science group. His research interests include web search, applied machine learning, and social media mining. Yi has published more than 70 conference/journal papers, and he is a co-author of the book, Relevance Ranking for Vertical Search Engines. Yi is an associate editor for Neurocomputing, Pattern Recognition Letters, and he has served as workshops co-organizers, conference organizer committee members, and area chairs for multiple conferences, including WWW, SIGIR, ICML, KDD, CIKM, etc.