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Computers & chemical engineering, v.33 no.2, 2009년, pp.429 - 435
Yan, Shuo (Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario, Canada M5S 3E5) , Sayad, Saed (Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario, Canada M5S 3E5) , Balke, Stephen T. (Corresponding author. Tel.: +1 416 978 7495)
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AbstractProcess monitoring using imaging can provide valuable information. However, the large number of images obtained necessitate automated classification into those showing “good” and “bad” product. This paper shows how a database of reference images can be used to modify ...
Ai Communications Aamodt 7 1 39 1994 10.3233/AIC-1994-7104 Case-based reasoning-Foundational issues, methodological variations, and system approaches
The American Statistician Agresti 52 2 119 1998 10.2307/2685469 Approximate is better than “exact” for interval estimation of binomial proportions
An, J.-L., Wang, Z.-O., & Ma, Z.-P. (2003). An incremental learning algorithm for support vector machine. Paper presented at the 2003 International Conference on Machine Learning and Cybernetics.
International Journal of Imaging Systems and Technology Bartels 8 2 214 1997 10.1002/(SICI)1098-1098(1997)8:2<214::AID-IMA8>3.0.CO;2-D Automated microscopy in diagnostic histopathology: From image processing to automated reasoning
Pattern Recognition Letters Bruzzone 20 11-13 1241 1999 10.1016/S0167-8655(99)00091-4 An incremental-learning neural network for the classification of remote-sensing images
IEEE Transactions on Neural Networks Carpenter 3 5 698 1992 10.1109/72.159059 Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps
Analytica Chimica Acta Dieterle 490 1-2 71 2003 10.1016/S0003-2670(03)00338-6 Growing neural networks for a multivariate calibration and variable selection of time-resolved measurements
Engineering Applications of Artificial Intelligence Ficet-Cauchard 12 6 733 1999 10.1016/S0952-1976(99)00041-X CBR for the management and reuse of image-processing expertise: a conversational system
Journal of Computer and System Sciences Freund 55 1 119 1997 10.1006/jcss.1997.1504 A decision-theoretic generalization of on-line learning and an application to boosting
IEEE Transactions on Neural Networks Fu 7 3 757 1996 10.1109/72.501732 Incremental backpropagation learning networks
10.1007/BFb0020609 Grimnes, M., & Aamodt, A. (1996). A two layer case-based reasoning architecture for medical image understanding. In Advances in Case-Based Reasoning (Vol. 1168, pp. 164-178).
Artificial Intelligence in Medicine Haddad 9 1 61 1997 10.1016/S0933-3657(96)00361-2 Feasibility analysis of a case-based reasoning system for automated detection of coronary heart disease from myocardial scintigrams
Ing, L., & Balke, S. T. (2002). In-line measurement of dispersed phase properties using the scanning particle monitor. Paper presented at the ANTEC 2002, San Francisco.
10.1007/BFb0056325 Jarmulak, J. (1998). Case-based classification of ultrasonic B-scans: Case-base organisation and case retrieval. In Advances in case-based reasoning. (Vol. 1488, pp. 100-111).
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Paper presented at the Proceedings of International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada.
Leake 1996 Case-Based Reasoning: Experiences, Lessons, and Future Directions
Investigative Radiology Macura 29 4 497 1994 10.1097/00004424-199404000-00020 Computerized case-based instructional-system for computed-tomography and magnetic-resonance-imaging of brain-tumors
10.1007/3-540-44593-5_28 McSherry, D. (2001). Precision and recall in interactive case-based reasoning. In Case-Based Reasoning Research and Development, Proceedings (Vol. 2080, pp. 392-406).
Fuzzy Sets and Systems Mouchaweh 132 1 49 2002 10.1016/S0165-0114(02)00060-X Incremental learning in fuzzy pattern matching
Perner 45 1998 Advances in pattern recognition Using CBR Learning for the low-level and highlevel unit of an image interpretation system
10.1007/3-540-48508-2_38 Perner, P. (1999). An architecture for a CBR image segmentation system. In Case-based reasoning research and development (Vol. 1650, pp. 525-534).
10.1007/3-540-44527-7_41 Perner, P. (2001a). CBR-based ultra sonic image interpretation. In Advances in case-based reasoning, Proceedings (Vol. 1898, pp. 479-490).
10.1007/3-540-44593-5_3 Perner, P. (2001b). Why case-based reasoning is attractive for image interpretation. In Case-based reasoning research and development, Proceedings (Vol. 2080, pp. 27-43).
Engineering Applications of Artificial Intelligence Perner 15 2 193 2002 10.1016/S0952-1976(02)00020-9 Are case-based reasoning and dissimilarity-based classification two sides of the same coin?
Sayad 2003 Proceedings of the Fourth International Conference on Data Mining, Data Mining IV An intelligent learning machine
Computers & Chemical Engineering Torabi 30 1 18 2005 10.1016/j.compchemeng.2005.06.008 On-line adaptive Bayesian classification for in-line particle image monitoring in polymer film manufacturing
Yan, S. (2007). Adaptive image quality improvement with Bayesian classification for in-line monitoring. University Toronto, Toronto, Ontario, Canada.
Computers and Chemical Engineering Yan 33 421 2009 10.1016/j.compchemeng.2008.10.013 Image quality in image classification: Design and construction of an Image Quality Database
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