Machine learning for interactive systems and robots: a brief introduction
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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Transfer learning has attracted increasing attention in machine learning, data mining, and many application areas. It is well-known that features sampled from different domains may differ tremendously in their distributions, or that labels across different tasks may be different. Consequently, a model trained on one domain or task cannot be applied to other domains or tasks precisely. Transfer learning is proposed to address the domain or task difference issue by extracting and transferring common knowledge across domains or tasks. We can find many novel applications of machine learning and data mining where transfer learning is helpful, especially when we have limited labeled data in the domain or task of interest. In this talk, I will first give an overview of transfer learning, and then present three specific transfer learning methods with applications to opinion mining from text, image classification using auxiliary text information, and indoor Wifi-based localization.