R.A. Amar, D.R. Dooly, S.A. Goldman, and Q. Zhang. Multiple-instance learning of real-valued data. In Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA, pp.3--10, 2001. Home/Search Document Details and Download
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Ensembles of Multi-Instance Learners - Zhou, Zhang (2003) (2 citations) Self-citation (Zhang) (Correct)
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Amar, R.A., Dooly, D.R., Goldman, S.A., Zhang, Q.: Multiple-instance learning of real-valued data. In: Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA (2001) 3--10
Ensembles of Multi-Instance Learners - Zhou, Zhang (2003) (2 citations) Self-citation (Zhang) (Correct)
No context found.
Amar, R.A., Dooly, D.R., Goldman, S.A., Zhang, Q.: Multiple-instance learning of real-valued data. In: Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA (2001) 3--10
Neural Networks for Multi-Instance Learning - Zhou, Zhang (2002) Self-citation (Zhang) (Correct)
....RIPPER. Recently, some researchers begin to investigate multiinstance regression tasks with real valued outputs. Ray and Page [12] showed that the general formulation of the multi instance regression task is NP hard, and proposed an EM based multi instance regression algorithm. Amar et al. [1] extended Diverse Density for multi instance regression and designed a method for artificially generating data sets for multi learning regression. It is worth noting that when the term multi instance learning was coined, Dietterich et al. 7] indicated that a particular interesting issue is how ....
R. A. Amar, D. R. Dooly, S. A. Goldman, and Q. Zhang. "Multiple-instance learning of real-valued data," in: Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA, pp.3-10, 2001.
EM-DD: An Improved Multiple-Instance Learning Technique - Zhang, Goldman (2001) (3 citations) Self-citation (Goldman Zhang) (Correct)
....real value classification of binding strength is preferable to a binary one. Most prior research on MI learning is restricted to concept learning (i.e. boolean labels) Recently, MI learning with real value labels has been performed using extensions of the diverse density (DD) and k NN algorithms [1] and using MI regression [10] In this paper, we present a general purpose MI learning technique (EM DD) that combines EM [3] with the extended DD [1] algorithm. The algorithm is applied to both boolean and real value labeled data and the results are compared with corresponding MI learning ....
.... boolean labels) Recently, MI learning with real value labels has been performed using extensions of the diverse density (DD) and k NN algorithms [1] and using MI regression [10] In this paper, we present a general purpose MI learning technique (EM DD) that combines EM [3] with the extended DD [1] algorithm. The algorithm is applied to both boolean and real value labeled data and the results are compared with corresponding MI learning algorithms from previous work. In addition, the effects of the number of instances per bag and the number of relevant features on the performance of EM DD ....
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Amar, R.A., Dooly, D.R., Goldman, S.A. & Zhang, Q. (2001). Multiple-Instance Learning of Real-Valued Data. Proceedings 18th International Conference on Machine Learning, pp. 3--10. San Francisco, CA: Morgan Kaufmann.
EM-DD: An Improved Multiple-Instance Learning Technique.. - Zhang, Goldman (2001) (3 citations) Self-citation (Goldman Zhang) (Correct)
.... activity prediction problem 1 where each instance is a possible con guration (or shape) for a molecule of interest and each bag (example) contains all low energy (and hence likely) con gurations for the molecule [4] There has been a signi cant amount of research directed towards this problem [1,4,5,6,9,11]. Maron and Raton [7] applied the multiple instance model to the task of learning to recognize a person from a series of images that are labeled positive if they contain the person and negative otherwise. They have also applied this model to learn descriptions of natural images (such as a ....
.... While the musk data sets have boolean labels (bind or not bind) binding anity between molecules and receptors is quantitative, and hence a real valued label giving the binding strength is preferable Recently some work on multipleinstance learning with real valued labels has been performed [1,10]. In fact, the work of Ray and Page [10] on multiple instance regression applies EM in a very similar way as we do except they consider a regression task. In addition to its superior performance on the musk data sets, EM DD uses real valued labels 2 Table 1: Comparison of performance on Musk1 and ....
[Article contains additional citation context not shown here]
Amar, R.A., Dooly, D.R., Goldman, S.A. & Zhang, Q. (2001). Multiple-Instance Learning of Real-Valued Data. Proceedings 18th International Conference on Machine Learning, to appear. San Francisco, CA: Morgan Kaufmann.
Multi-Instance Learning Based Web Mining - Zhi-Hua Zhou Kai (Correct)
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R.A. Amar, D.R. Dooly, S.A. Goldman, and Q. Zhang. Multiple-instance learning of real-valued data. In Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA, pp.3--10, 2001.
Multi-Instance Learning Based Web Mining - Zhou, Jiang, Li (Correct)
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R.A. Amar, D.R. Dooly, S.A. Goldman, and Q. Zhang. Multiple-instance learning of real-valued data. In Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA, pp.3--10, 2001.
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