Abstract
Multiple-Instance Learning is increasingly becoming one of the most promiscuous research areas in machine learning. In this paper, a new algorithm named NRBF-MI is proposed for Multi-Instance Learning based on normalized radial basis function network. This algorithm defined Compact Neighborhood of bags on which a new method is designed for training the network structure of NRBF-MI. The behavior of kernel function radius and its influence is analyzed. Furthermore a new kernel function is also defined for dealing with the labeled bags. Experimental results show that the NRBF-MI is a high efficient algorithm for Multi-Instance Learning.
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References
Dietterich, T.G., Lathrop, R.H., Pérez, T.L.: Solving the Multiple-instance Problem with Axis-parallel Rectangles. Artificial Intelligence 89(1-2), 31–71 (1997)
Maron, O.: Learning from Ambiguity. PhD Dissertation. Department of Electrical Engineering and Computer Science, MIT (1998)
Long, P.M., Tan, L.: PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples. Machine Learning 30(1), 7–21 (1998)
Auer, P.: On Learning from Multi-instance Examples: Empirical Evaluation of a Theoretical Approach. In: Proceedings of the 14th International Conference on Machine Learning, Nashville, TN, pp. 21–29 (1997)
Blum, A., Kalai, A.: A Note on Learning from Multiple-instance Examples. Machine Learning 30(1), 23–29 (1998)
Maron, O., Pérez, T.L.: A Framework for Multiple-instance Learning. In: Jordan, M.I., Kearns, M.J., Solla, S.A. (eds.) Advances in Neural Information Processing Systems, vol. 10, pp. 570–576. MIT Press, Cambridge (1998)
Zhang, Q., Goldman, S.A.: EM-DD: An Improved Multiple Instance Learning Technique. Neural Information Processing Systems 14 (2001)
Wang, J., Zucker, J.D.: Solving the Multiple-instance Problem: A Lazy Learning Approach. In: Proceedings of the 17th International Conference on Machine Learning, San Francisco, CA, pp. 1119–1125 (2000)
Zhou, Z.H., Zhang, M.L.: Neural Networks for Multi-instance Learning. Technical Report, AI Lab, Computer Science & Technology Department, Nanjing University. China (Aug. 2002)
Edgar, G.A.: Measure, Topology, and Fractal Geometry, 3rd edn. Springer, Heidelberg (1995)
Kim, N., Byun, H.G., Kwon, K.H.: Learning Behaviors of Stochastic Gradient Radial Basis Function Network Algorithms for Odor Sensing System. ETRI Journal 28(1), 59–66 (2006)
Dooly, D.R., Zhang, Q., Amar, R.A.: Multiple-Instance Learning of Real-Valued Data. Journal of Machine Learning Research 3 (2002)
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Chai, YM., Yang, ZW. (2007). A Multi-Instance Learning Algorithm Based on Normalized Radial Basis Function Network. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_136
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DOI: https://doi.org/10.1007/978-3-540-72383-7_136
Publisher Name: Springer, Berlin, Heidelberg
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