In this chapter, we first give the background for writing this monograph. Then, we provide a formal definition of multiview machine learning and discuss its difference and similarities with related concepts data fusion and multimodal learning. After showcasing four typical application fields in artificial intelligence, we explain the underlying philosophy on why multiview learning is useful. Finally, we give the organization structure of the book.
- Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th annual conference on computational learning theory, ACM, pp 92–100Google Scholar
- Nigam K, Ghani R (2000) Analyzing the effectiveness and applicability of co-training. In: Proceedings of the 9th international conference on information and knowledge management, ACM, pp 86–93Google Scholar
- Sun S, Chao G (2013) Multi-view maximum entropy discrimination. In: Proceedings of the 23rd international joint conference on artificial intelligence, pp 1706–1712Google Scholar