J.A. Benediktsson, J.R. Sveinsson, O.K. Ersoy, and P.H. Swain. Parallel consensual neural networks with optimally weighted outputs. In Proceedings of the World Congress on Neural Networks, pages III:129--137. INNS Press, 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... combining multiple classifiers is via simple averaging of the corresponding output values (Lincoln and Skrzypek, 1990; Perrone and Cooper, 1993b; Tumer and Ghosh, 1996) Weighted averaging has also been proposed, and different methods for computing the proper classifier weights have been studied (Benediktsson et al. 1994; Hashem and Schmeiser, 1993; Jacobs, 1995; Lincoln and Skrzypek, 1990) Such linear combining techniques have been mathematically analyzed both for classification (Tumer and Ghosh, 1995c; Tumer and Ghosh, 1996) and regression problems (Perrone and Cooper, 1993a; Hashem and Schmeiser, 1993) Some ....

Benediktsson, J., Sveinsson, J., Ersoy, O., and Swain, P. (1994). Parallel consensual neural networks with optimally weighted outputs. In Proceedings of the World Congress on Neural Networks, pages III:129--137. INNS Press.

....provide the combined output f comb . Currently, the most popular way of combining multiple classifiers is via simple averaging of the corresponding output values [19, 27, 33, 46] Weighted averaging has also been proposed, along with different methods of computing the proper classifier weights [6, 21, 24, 27]. Such linear combining techniques have been mathematically analyzed for regression problems [21, 32] but not for classification. Some researchers have investigated non linear combiners using rank based information [1, 23] or voting schemes [4] Methods for combining 2 f ind f comb Classifier ....

J.A. Benediktsson, J.R. Sveinsson, O.K. Ersoy, and P.H. Swain. Parallel consensual neural networks with optimally weighted outputs. In Proceedings of the World Congress on Neural Networks, pages III:129--137. INNS Press, 1994.

.... a majority vote for independent classifiers [70] Hampshire and Waibel use the normalized sum of three networks trained on different objective functions for speech recognition, and propose a modular structure where different modules are trained with different speakers [68, 69] Benediktsson et al. [21] linearly combine neural networks trained on non linearly transformed versions of the original data to improve the classification performance. Lincoln and Skrzypek study the performance and fault tolerance of network ensembles, and propose a new learning rule specifically designed for network ....

J. Benediktsson, J. Sveinsson, O. Ersoy, and P. Swain, Parallel consensual neural networks with optimally weighted outputs, in Proceedings of the World Congress on Neural Networks, INNS Press, 1994, pp. III:129--137.

.... The concept of combining has been studied in the connectionist framework in several forms, including stacked generalization (Wolpert 1992) and ensemble methods (Hampshire Waibel 1992; Lincoln Skrzypek 1990; Perrone Cooper 1993; Tumer Ghosh 1996c) Concepts such as weighted averaging (Benediktsson et al. 1994; Hashem Schmeiser 1993; Jacobs 1995; Lincoln Skrzypek 1990) rank based combining (Al Ghoneim Vijaya Kumar 1995; Ho, Hull, Srihari 1994) belief based combining (Rogova 1994; Xu, Krzyzak, Suen 1992; Yang Singh 1994) have also been analyzed. Combining has also been studied in other ....

Benediktsson, J.; Sveinsson, J.; Ersoy, O.; and Swain, P. 1994. Parallel consensual neural networks with optimally weighted outputs. In Proceedings of the World Congress on Neural Networks, III:129--137. INNS Press.

No context found.

J.A. Benediktsson, J.R. Sveinsson, O.K. Ersoy, and P.H. Swain. Parallel consensual neural networks with optimally weighted outputs. In Proceedings of the World Congress on Neural Networks, pages III:129--137. INNS Press, 1994.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC