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Built upon the existing literature on the max margin-based learning, we developed a new max margin learning approach called Enhanced Max Margin Learning (EMML) framework.
About
In addition, the application of the MML framework developed an effective and efficient solution to the multimodal data mining problem in a multimedia database. The main contributions include: developing a new max margin learning approach which the enhanced max margin learning framework is much more efficient in learning with a much faster convergence rate, which is verified in empirical evaluations. By applying this EMML approach lead to an effective and efficient solution in which the multimodal data mining problem which is highly scalable in the sense that the query response time is independent of the database scale. Therefore allowing facilitating a multimodal data mining querying to a very large scale multimedia database, and excelling many existing multimodal data mining methods in the literature that do not scale up at all. While EMML is a general framework, for the evaluation purpose, we apply it to the Berkeley Drosophila embryo image database, and report the performance comparison with a state-of-the-art multimodal data mining method.
Key Benefits
• Built upon the existing literature on the max margin-based learning, EMML is much more efficient in learning with a much faster convergence ate. • EMML approach can be used to developing an effective and efficient solution to the multimodal data mining problem that is highly scalable in the sense that the query response time is independent of the database scale, allowing facilitating a multimodal data mining querying Ito a very large-scale multimedia database and excelling many existing multimodal data mining methods in the literature that do not scale up at all.