Abstract
This paper presents a decoupled two stage solution to the multiple-instance learning (MIL) problem. With a constructed affinity matrix to reflect the instance relations, a modified Random Walk on a Graph process is applied to infer the positive instances in each positive bag. This process has both a closed form solution and an efficient iterative one. Combined with the Support Vector Machine (SVM) classifier, this algorithm decouples the inferring and training stages and converts MIL into a supervised learning problem. Compared with previous algorithms on several benchmark data sets, the proposed algorithm is quite competitive in both computational efficiency and classification accuracy.
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Keywords
- Support Vector Machine
- Positive Instance
- Negative Instance
- Multiple Instance Learning
- Support Vector Machine Parameter
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Wang, D., Li, J., Zhang, B. (2006). Multiple-Instance Learning Via Random Walk. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Machine Learning: ECML 2006. ECML 2006. Lecture Notes in Computer Science(), vol 4212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871842_45
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DOI: https://doi.org/10.1007/11871842_45
Publisher Name: Springer, Berlin, Heidelberg
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