Smith, K., Palaniswami, M., and Krishnamoorthy, M. (1998). Neural techniques for combinatorial optimization with applications. IEEE Transactions on Neural Networks, 9:1301--1318. Home/Search Document Details and Download
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Comments on “Approximation Capability in C(Rn) by Multilayer.. - Huang, Babri (1998) (Correct)
....allow for temporary energy increases, such as simulated annealing [7] have been proposed. Recent advances have now made modified HNN s competitive with the best heuristics for solving combinatorial optimization problems, and this has been demonstrated on a variety of real world problems [10] [11]. The search still continues however, for further or alternative improvements to the standard Manuscript received November 9, 1997. This work was supported in part by the Australian Research Council and Deakin University. L. Wang is with the School of Computing and Mathematics, Deakin University, ....
K. Smith, M. Palaniswami, and M. Krishnamoorthy, "Neural techniques for combinatorial optimization with applications," IEEE Trans. Neural Networks, accepted.
Adaptive and Neural Methods for Image Segmentation - Ghosh (2000) (1 citation) (Correct)
.... the probability that this minima is a poor or even invalid solution increases rapidly with increase in problem size (number of variables) Fortunately, related but more sophisticated and powerful schemes for optimization have emerged recently, and can be readily applied for texture segmentation [7, 8]. 3 Perceptual Grouping and Edge based Segmentation. We perceive an image not as an array of pixels but as agglomerations or groupings of more abstract entities. A sharp spatial gradient in gray scale at an image location may not lead to the perception of an edge at that location if similar ....
....the Hop eld Tank formulation, they were plagued by large training times, and high possibility of being caught in local minima, leading to poor solutions. Fortunately, more sophisticated and powerful schemes for optimization have emerged recently, and can be readily applied for texture segmentation [7, 8]. The MRF framework can also directly leverage the powerful mapping capabilities of feedforward networks. For example, Hwang Chen has used an MLP to directly obtain the class distributions conditional on the neighborhood image statistics (needed for the MRF) based on training image samples. ....
K. Smith, M. Paliniswami, and M. Krishnamoorthy. Neural techniques for combinatorial optimization with applications. 9(6):1301-09, 1998.
Bibliography of Self-Organizing Map (SOM) Papers.. - Merja Oja, Samuel.. (2002) (Correct)
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Smith, K., Palaniswami, M., and Krishnamoorthy, M. (1998). Neural techniques for combinatorial optimization with applications. IEEE Transactions on Neural Networks, 9:1301--1318.
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