The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Content-based hierarchical document organization using multi-layer hybrid network and tree-structured features
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“Tree-structured features representation is a rich data characterization, and ML-SOM are very effective in handling such contextual information [134], [142].”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [142]
Citations
Cites
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Term-weighting approaches in automatic text retrieval
Salton, G., & Buckley, C. (1988) · Information Processing & Management · doi:10.1016/0306-4573(88)90021-0 · Cited by 9,740
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,248
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096
Using Linear Algebra for Intelligent Information Retrieval
Berry, M. W., Dumais, S. T., & O’Brien, G. W. (1995) · SIAM Review · doi:10.1137/1037127 · Cited by 1,481
Papadimitriou, C. H., Tamaki, H., Raghavan, P., & Vempala, S. (1998) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/275487.275505 · Cited by 862
Hierarchically Classifying Documents Using Very Few Words
Koller, D., & Sahami, M. (1997) · Cited by 854
Hierarchical classification of Web content
Dumais, S., & Chen, H. (2000) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/345508.345593 · Cited by 810
Salembier, P., & Garrido, L. (2000) · IEEE Transactions on Image Processing · doi:10.1109/83.841934 · Cited by 587
Latent Semantic Indexing: A Probabilistic Analysis
Papadimitriou, C. H., Raghavan, P., Tamaki, H., & Vempala, S. (2000) · Journal of Computer and System Sciences · doi:10.1006/jcss.2000.1711 · Cited by 577
WEBSOM – Self-organizing maps of document collections
Kaski, S., Honkela, T., Lagus, K., & Kohonen, T. (1998) · Neurocomputing · doi:10.1016/s0925-2312(98)00039-3 · Cited by 495
A comparison of classifiers and document representations for the routing problem
Schütze, H., Hull, D. A., & Pedersen, J. O. (1995) · Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '95 · doi:10.1145/215206.215365 · Cited by 453
Yang, Y. (1994) · Springer eBooks · doi:10.1007/978-1-4471-2099-5_2 · Cited by 386
Exploring the similarity space
Zobel, J., & Moffat, A. (1998) · ACM SIGIR Forum · doi:10.1145/281250.281256 · Cited by 378
Narrowing the semantic gap - improved text-based web document retrieval using visual features
Zhao, R., & Grosky, W. I. (2002) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2002.1017733 · Cited by 248
A self-organizing map for adaptive processing of structured data
Hagenbuchner, M., Sperduti, A., & Tsoi, A. C. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.810735 · Cited by 180
Intellectual structure of research in organizational behavior, 1972-1984: A cocitation analysis
Culnan, M. J., O’Reilly, C. A., & Chatman, J. A. (1990) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9 · Cited by 178
Exploiting Hierarchy in Text Categorization
Weigend, A. S., Wiener, E. D., & Pedersen, J. (1999) · Information Retrieval · doi:10.1023/a:1009983522080 · Cited by 159
Srinivasan, P. (1996) · Information Processing & Management · doi:10.1016/0306-4573(95)00076-3 · Cited by 112
Automatic document classification and indexing in high-volume applications
Appiani, E., Cesarini, F., Colla, A. M., Diligenti, M., Gori, M., Marinai, S., & Soda, G. (2001) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/pl00010904 · Cited by 43
Cho, S., Chi, Z., Siu, W.-C., & Tsoi, A. C. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.813831 · Cited by 41
Content-based image retrieval by using tree-structured features and multi-layer self-organizing map
Chow, T. W. S., Rahman, M. K. M., & Wu, S. (2006) · Pattern Analysis and Applications · doi:10.1007/s10044-005-0019-1 · Cited by 37
Self-citationGenetic evolution processing of data structures for image classification
Cho, S.-Y., & Chi, Z. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.28 · Cited by 32
A flexible multi-layer self-organizing map for generic processing of tree-structured data
Rahman, M. K. M., Yang, W. P., Chow, T. W. S., & Wu, S. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.10.010 · Cited by 24
Self-citationWeb Content Management by Self-Organization
Freeman, R. T., & Yin, H. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.853415 · Cited by 19
LSISOM — A Latent Semantic Indexing Approach to Self-Organizing Maps of Document Collections
Ampazis, N., & Perantonis, S. J. (2004) · Neural Processing Letters · doi:10.1023/b:nepl.0000023449.95030.8f · Cited by 18 · cited 3×
“This causes a significant increase in computational burden making the VSM model impractical for handling large database.”
BackgroundMethodMerkl, D., He, S. H., Dittenbach, M., & Rauber, A. (2003) · Cited by 18
Image classification with structured self-organization map
Wang, Z., Hagenbuchner, M., Tsoi, A. C., Cho, S. Y., & Chi, Z. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2002.1007812 · Cited by 12
Rauber, A., Dittenbach, M., & Merkl, D. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45268-0_37 · Cited by 12
Hagenbuchner, M. (2002) · Research Online (University of Wollongong) · Cited by 5
Cited by
Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods
Alzahrani, S. M., Salim, N., & Abraham, A. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2134847 · Cited by 299
An improved plagiarism detection scheme based on semantic role labeling
Osman, A. H., Salim, N. B., Binwahlan, M. S., Alteeb, R., & Abuobieda, A. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2011.12.021 · Cited by 98
Dimoulas, C. A., Papanikolaou, G. V., & Petridis, V. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.04.115 · Cited by 52
Survey of Text Plagiarism Detection
Osman, A. H., Salim, N. B., & Abuobieda, A. (2012) · Computer Engineering and Applications Journal · doi:10.18495/comengapp.v1i1.5 · Cited by 37
Feature unionization: A novel approach for dimension reduction
Jalilvand, A., & Salim, N. B. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.08.031 · Cited by 30
Metadata
- Authors
- Rahman, M. K. M., & Chow, T. W. S.
- Affiliations
- City University of Hong Kong (HK)
- Corresponding author
- Chow, Tommy W.S.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2010-04
- Volume
- 37
- Issue
- 4
- Pages
- 2874-2881
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2150838259
- Cited by
- 11
- References
- 30
- Citations by year
- 2021: 1 · 2020: 1 · 2018: 2 · 2016: 1 · 2013: 1 · 2012: 2
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Expert Systems with Applications (publishedVersion)
- Funders
- City University of Hong Kong
- University of Hong Kong
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- hybrid neural network, self-organizing maps, multilayer perceptron, document structure, document semantics
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.