`a`
Big Data and Information Analytics (BDIA)
 

Multiple-instance learning for text categorization based on semantic representation
Pages: 69 - 75, Issue 1, January 2017

doi:10.3934/bdia.2017009      Abstract        References        Full text (318.1K)           Related Articles

Jian-Bing Zhang - National Key Laboratory for Novel Software Technology, Nanjing University, China (email)
Yi-Xin Sun - National Key Laboratory for Novel Software Technology, Nanjing University, China (email)
De-Chuan Zhan - National Key Laboratory for Novel Software Technology, Nanjing University, China (email)

1 J. Amores, Multiple instance classification: Review, taxonomy and comparative study, Artificial Intelligence, 201 (2013), 81-105.       
2 S. Andrews, I. Tsochantaridis and T. Hofmann, Support vector machines for multiple-instance learning, Advances in Neural Information Processing Systems, 15 (2002), 561-568.
3 W. B. Cavnar, J. M. Trenkle, et al., N-gram-based text categorization, Ann Arbor MI, 48113 (1994), 161-175.
4 Y. Chevaleyre and J. D. Zucker, Solving multiple-instance and multiple-part learning problems with decision trees and rule sets. application to the mutagenesis problem, In Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence, (2001), 204-214.
5 T. G. Dietterich, R. H. Lathrop and T. Lozano-Pérez, Solving the multiple instance problem with axis-parallel rectangles, Artificial Intelligence, 89 (1997), 31-71.
6 S. Dumais, Using svms for text categorization, IEEE Expert, 13 (1998), 21-23.
7 N. Ishii, T. Murai, T. Yamada and Y. Bao, Text classification by combining grouping, lsa and knn, In Ieee/acis International Conference on Computer and Information Science and Ieee/acis International Workshop on Component-Based Software Engineering,software Architecture and Reuse, (2006), 148-154.
8 Q. Kuang and X. Xu, Improvement and application of tfidf method based on text classification, In International Conference on Internet Technology and Applications, (2010), 1-4.
9 S. Lai, L. Xu, K. Liu and J. Zhao, Recurrent convolutional neural networks for text classification, In AAAI, (2015), 2267-2273.
10 O. Maron and T. Lozano-Pérez, A framework for multiple-instance learning, Advances in Neural Information Processing Systems, 200 (1998), 570-576.
11 A. Mccallum and K. Nigam, A comparison of event models for naive bayes text classification, In AAAI-98 Workshop On Learning For Text Categorization, 62 (2009), 41-48.
12 T. Mikolov, K. Chen, G. Corrado and J. Dean, Efficient estimation of word representations in vector space, Computer Science, 2013.
13 T. Mikolov, I. Sutskever, K. Chen, G. Corrado and J. Dean, Distributed representations of words and phrases and their compositionality, Advances in Neural Information Processing Systems, 26 (2013), 3111-3119.
14 J. Wang and J. D. Zucker, Solving multiple-instance problem: A lazy learning approach, Proc.international Conf.on Machine Learning, (2000), 1119-1126.
15 M. L. Zhang and Z. H. Zhou, Improve multi-instance neural networks through feature selection, Neural Processing Letters, 19 (2004), 1-10.
16 Z. H. Zhou and M. L. Zhang, Neural networks for multi-instance learning, In International Conference on Intelligent Information Technology, 2002.

Go to top