J. J. Merelo and A. Prieto. G-LVQ, a combination of genetic algorithms and LVQ. In Pearson et al. [999], pages 92--95. ga95aMerelo.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....John, 614, 615, 616] McInerney, Michael, 805] McInerney, M. 62] Mecklenburg, Klaus, 910] Meeden, Lisa A. 455] Meisel, J. 417] Melsheimer, S. S. 55] Menczer, Filippo, 834, 835, 836] Mendoca, P. R. S. 480] Mendonca, P. R. S. 317] Meng, Qing chun, 277] Merelo, J. J. [153, 198, 293, 513, 865, 866] Meservy, R. D. 346] Meyer, Claudia M. 867, 868] Meyer, Jean Arcady, 772] Michel, Olivier, 200] Middleton, L. T. 837] Miglino, Orazio, 63, 296, 862] Mihaila, D. 298] Miikkulainen, Risto, 118, 838, 839] Mikami, Sadayoshi, 553, 574] Miller, Geoffrey F. 37, 711, 712, 713] ....

....W. 276, 681, 685, 687, 879, 880] Poshyanonda, Pipatpong, 75] Postaire, Jack Gerard, 586] Potter, Mitchell A. 199] Potvin, Jean Yves, 467] Powell, William A. 26] Poza, M. 527] Prados, D. L. 881] Prasanth, Ravi K. 944] Pratt, P. 76] Price, J. E. 456] Prieto, A. [153, 198, 293, 513, 865, 866] Protzel, P. 416] Puigjaner, L. 329] Pyeatt, Larry, 303, 341] Qiang, Wang, 457] Qizhi, Zhang, 374] Rabelo, L. C. 255] Rabelo, Luis, 207] Radcliffe, Nicholas J. 885, 886, 887] Ragg, T. 381] Rajasekaran, S. 382, 541] Rajroop, P. 57] Ramasamy, J. V. 382] RamBabu, P. ....

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J. J. Merelo and A. Prieto. G-LVQ, a combination of genetic algorithms and LVQ. In Pearson et al. [999], pages 92--95. ga95aMerelo.

....Javier, 138, 145, 251, 252] Marques, R. M. Lopes, 132] Martin, C. A. 21, 27] Martin, F. J. Marin, 173] Martin Bautista, M. J. 228] Martins, Weber, 29] McInnes, F. R. 20] Mendes, R. 112] Mendoca, P. R. S. 55] Mendonca, P. R. S. 39] Mercedes, de Las, 216] Merelo, J. J. [155, 182, 193, 222, 223, 250] Michaelian, K. 91, 101] Miliani, L. 265] Miranda, V. 113, 115, 116, 117, 123, 125, 64] Monasterio, F. 154, 167] Monasterio Huelin, F. 234] Montenegro, Anselmo A, 31] Montero, G. 135, 142] Montilla, Guillermo, 265, 266] Morales, D. 162] Moran, F. 223, 238, 250] Moreira, D. de ....

....E. 238] Parrazales, R. U. 89] Paton, A. 250] Pecas Lopes, J. A. 118] Peregr in, A. 177] Pereira, Francisco, 130] Pereira, M. V. F. 37] Perez, J. M. S. 183] Perez, Raul, 233] Petridis, V. 184] Pina, A. 129] Poch, M. 232] Poza, Mikel, 200] Poza, M. 226] Prieto, A. [155, 182, 222, 223, 250] Proenca, A. 129] Proenca, Luis Miguel, 113, 115, 116, 117, 123, 125, 64] Puente, J. 208] Puigjaner, L. 187] Ramirez Jaramillo, E. 91] Ramos, Antonio Rogerio Machado, 32] Ramos, Vitorino, 131] Ram irez Rosaro, Ignacio J. 139, 169, 240] Rangel, Naykiavick, 265, 266] Ranito, J. ....

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J. J. Merelo and A. Prieto. G-LVQ, a combination of genetic algorithms and LVQ. In Pearson et al. [273], pages 92--95. ga95aMerelo.

....been applied succesfully to biological particle classification and reconstruction [4, 9] showing results that are more robust, and sometimes faster, than traditional statistical techniques. In this paper, we will apply to the above mentioned problem a previously described technique, g lvq [11], based in a two level genetic algoritm (GA) operating on variable size chromosomes, which codify the initial weights and labels for an lvq network; results will be compared to Kohonen s Learning Vector Quantization [7] lvq) algorithm for codebook training. We will first present the state of ....

....to optimize the size of other neural nets, like MLP trained with backpropagation [18] Specially noteworthy is g prop in its different variants [15, 16] whose results will be compared with g lvq in this paper. Previously, a method for global optimization of lvq was proposed by the authors in [11, 3]. That method has been streamlined and improved in this paper, giving the best results so far. With respect to the problem of classifying biological particles, usual pattern recognition techniques are a mixture of neural networks and personal experience, with some statistical techniques thrown in; ....

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J. J. Merelo and A. Prieto. g-lvq, a combination of genetic algorithms and lvq. In N.C.Steele D.W.Pearson and R.F.Albrecht, editors, Artificial Neural Nets and Genetic Algorithms, pages 92--95. Springer-Verlag, 1995.

....class. This approach has the advantage of not relying on threshold parameters, but it still has the problem of being a local search procedure, that optimizes size step by step; and besides, it relies on heuristics for the initial weights. A method for global optimization of LVQ was proposed in [11]. This method is simplified, taking out unnecesary features, and extended here to other kind of classifier training algorithms besides LVQ, testing it on real world problems taken from machine learning databases. 3 Method The method proposed here for global optimization of classifiers is based ....

....classifiers with useless parts will be worse than others than use all their components; and if adding a new structure to a classifier improves performance, it will be preferred in fitness evaluation. These two strategies (directed length alteration, used in a previous version of this work [11], and non directed length alteration) will be tested against each other. In any case, these directed operators can be used to fix classifiers resulting at the end of the combined GA classifier algorithm training. 3.3 Vectorial fitness As it has been said above, classifiers will be optimized ....

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J. J. Merelo and A. Prieto. G-lvq, a combination of genetic algorithms and lvq. In N.C.Steele D.W.Pearson and R.F.Albrecht, editors, Artificial neural Nets and Genetic Algorithms, pages 92--95. Springer-Verlag, 1995.

....the same specimen. Using two labeled sets as a reference, the parameters and architecture of the classifier were optimized using a genetic algorithm. The global automatic process of training and optimization is implemented using the previously described g lvq (genetic learning vector quantization) [10] algorithm, and compared to a non optimized version of the algorithm, Kohonen s lvq (learning vector quantization) 7] Using a part of the sample as training set, the results presented here show an efficient (approximately 90 ) average classification rate of unknown samples in two classes. The ....

....already been applied succesfully to biological particle classification and reconstruction [4, 8] showing results that are more robust, and sometimes faster, than traditional statistical techniques. In this paper, we will apply to the above mentioned problem a previously described technique, g lvq [10], based in a two level genetic algoritm (GA) operating on variable size chromosomes, which codify the initial weights and labels for an lvq network; results will be compared to Kohonen s Learning Vector Quantization [7] helicasas nuevo.tex; 30 07 1997; 9:34; no v. p.2 Automatic classification ....

[Article contains additional citation context not shown here]

J. J. Merelo and A. Prieto. g-lvq, a combination of genetic algorithms and lvq. In N.C.Steele D.W.Pearson and R.F.Albrecht, editors, Artificial Neural Nets and Genetic Algorithms, pages 92--95. Springer-Verlag, 1995.

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