Bensaid, A. M., Hall, L. O., Bezdek, J. C., & Clarke, L. P. (1996). Partially supervised clustering for image segmentation. Pattern Recognition, 29, 859--871. Home/Search Document Not in Database Summary Related Articles Check |
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Semi-Supervised Support Vector Machines - Bennett, Demiriz (1998) (34 citations) (Correct)
....Semi supervised learning occurs when both training and working sets are nonempty. Semi supervised learning for problems with small training sets and large working sets is a form of semi supervised clustering. There are successful semi supervised algorithms for k means and fuzzy c means clustering [4, 18]. Clustering is a potential application for S 3 VM as well. When the training set is large relative to the working set, S 3 VM can be viewed as a method for solving the transduction problem according to the principle of overall risk minimization (ORM) posed by Vapnik at the NIPS 1998 SVM ....
A.M. Bensaid, L.O. Hall, J.C. Bezdek, and L.P. Clarke. Partially supervised clustering for image segmentation. Pattern Recognition, 29(5):859--871, 199.
A What-and-Where Fusion Neural Network for.. - Granger, Rubin.. (2001) (Correct)
....pulse trains transmitted 14 by different emitters. This problem raises the question of whether the classifier may benefit from training on data with missing class labels. Training on such data is referred to as semisupervised learning (Demiriz et al. 1999) or partially supervised clustering (Bensaid, 1996; Pedrycz, 1985) To assess the effect on performance of training fuzzy ARTMAP using data with missing class labels, the network was trained in two phases (Granger et al. 2000) During the first phase, involving supervised learning, the network was trained as usual until convergence with a fixed ....
Bensaid, A. M., Hall, L. O., Bezdek, J. C., & Clarke, L. P. (1996). Partially Supervised Clustering for Image Segmentation. Pattern Recognition, 29, 859-871.
New Methods for Cluster Selection in Unsupervised Fuzzy.. - Cosic, Loncaric (1996) (1 citation) (Correct)
....the next section three methods for the new cluster center selection are presented and compared. 3 METHODS A number of supervised and unsupervised pattern recognition techniques including clustering algorithms have been proposed in recent years for segmentation and quantitative analysis of images [9, 10]. MRI segmentation clustering techniques are extensively researched in last years [11, 12] Some of the authors compared clustering techniques with other segmentation techniques such as neural networks [13, 7] The aim of this work is to apply the proposed techniques to the problem of segmentation ....
....fhv pd fhv pd fhv pd 1 89 14.9 89 14.0 89 14.0 2 178 7.4 43 25.0 43 25.0 3 267 4.9 198 22.0 196 22.0 4 356 3. 7 256 21.0 272 17.3 5 100 42.0 314 17.0 344 14.7 6 127 46.0 313 21.0 76 31.0 7 55 53.0 91 33.0 92 33.2 8 80 36.0 347 21.0 98 31.0 Table 1: Cluster validity measures: fuzzy hypervolume (fhv[10 3 ]) and partition density (pd[10 Gamma3 ] 5 CONCLUSION Three new methods for selection of a new cluster center in the K means fuzzy clustering algorithm are presented in this paper. The technique is applied to the problem of segmentation of images obtained by Computed Tomography (CT) ....
A.M. Bensaid, L. O. Hall, J.C. Bezdek, and L. P. Clarke. Partially supervised clustering for image segmentation. Pattern Recognition, 29:859--871, 1996.
New Methods for Cluster Selection in Unsupervised Fuzzy.. - Cosic, Loncaric (1996) (1 citation) (Correct)
....are described in Section III. A conclusion is provided in Section IV. II. Methods and Procedures A number of supervised and unsupervised pattern recognition techniques including clustering algorithms have been proposed in recent years for segmentation and quantitative analysis of images [2] [3]. The aim of this work is to apply the proposed techniques to the problem of segmentation of human spontaneous intracerebral brain hemorrhage (ICH) from CT images by means of unsupervised fuzzy K means clustering. The CT image segmentation plays a central role in quantitative analysis of ICH [4] ....
....fhv pd fhv pd fhv pd 1 89 14.9 89 14.0 89 14.0 2 178 7.4 43 25.0 43 25.0 3 267 4.9 198 22.0 196 22.0 4 356 3. 7 256 21.0 272 17.3 5 100 42.0 314 17.0 344 14.7 6 127 46.0 313 21.0 76 31.0 7 55 53.0 91 33.0 92 33.2 8 80 36.0 347 21.0 98 31.0 Table 1: Cluster validity measures: fuzzy hypervolume (fhv[10 3 ]) and partition density (pd[10 Gamma3 ] Figure 2: Experimental results. The first, second, and third column for methods 1, 2, and 3, respectively. methods 1, 2, and 3 respectively. The cluster validity measures are presented in Table 1. It is evident from the Table 1 that the best ....
A.M. Bensaid, L. O. Hall, J.C. Bezdek, and L. P. Clarke. Partially supervised clustering for image segmentation. Pattern Recognition, 29:859--871, 1996.
Learning to Extract Entities from Labeled and Unlabeled Text - Jones (2005) (Correct)
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Bensaid, A. M., Hall, L. O., Bezdek, J. C., & Clarke, L. P. (1996). Partially supervised clustering for image segmentation. Pattern Recognition, 29, 859--871.
Radar Esm With A What-And-Where Fusion Neural Network - Granger, Rubin, Grossberg.. (2001) (Correct)
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A. M. Bensaid, L. O.Hall, J. C. Bezdek & L. P. Clarke, "Partially supervised clustering for image segmentation," Pattern Recognition, vol. 29, pp. 859871, 1996.
Semi-Supervised Clustering Using Genetic Algorithms - Demiriz, Bennett (1999) (10 citations) (Correct)
No context found.
A.M. Bensaid, L.O. Hall, J.C. Bezdek, and L.P. Clarke. Partially supervised clustering for image segmentation. Pattern Recognition, 29(5):859-- 871, 1996. 19
Semi-Supervised Clustering Using Genetic Algorithms Ayhan.. - Demiriz, Bennett (1999) (10 citations) (Correct)
No context found.
A.M. Bensaid, L.O. Hall, J.C. Bezdek, and L.P. Clarke. Partially supervised clustering for image segmentation. Pattern Recognition, 29(5):859--871, 1996.
Medical Magnetic Resonance Imaging - Lundervold (1996) (1 citation) (Correct)
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A. M. Bensaid, L. O. Hall, J. C. Bezdek, and L. P. Clarke. Partially supervised clustering for image segmentation. Pattern Recognition, 29:859--871, 1996.
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