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Unified and scalable learning in multimedia information retrieval

Published: 26 October 2006 Publication History

Abstract

Statistical-learning approaches such as unsupervised learning, supervised learning, active learning, and reinforcement learning have generally been separately studied and applied to solve application problems. In this talk, I will present our recent work on a unified learning paradigm (ULP). ULP is motivated by how human being acquires knowledge: we learn by being taught (supervised learning), by self-study (unsupervised learning), by asking questions (active learning), and by being examined for the ability to generalize (reinforcement learning). I will present our recent ICML and KDD work on ULP, which can substantially reduce the amount of required training data. I will also present our proposed algorithmic and data processing techniques for speeding up kernel-based learning (ICML, KDD, SIAM, and MM) for multimedia information retrieval. Finally, I will touch basis on my work at Google that relates to the multimedia community.

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  1. Unified and scalable learning in multimedia information retrieval

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      cover image ACM Conferences
      MIR '06: Proceedings of the 8th ACM international workshop on Multimedia information retrieval
      October 2006
      344 pages
      ISBN:1595934952
      DOI:10.1145/1178677
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      New York, NY, United States

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      Published: 26 October 2006

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      MM06
      MM06: The 14th ACM International Conference on Multimedia 2006
      October 26 - 27, 2006
      California, Santa Barbara, USA

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