Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The rate of convergence of net output error is very low when training feedforward neural networks for multiclass problems using the backpropagation algorithm. While backpropagation will reduce the Euclidean distance beten the actual and desired output vectors, the differences beten some of the components of these vectors increase in the first iteration. Furthermore, the magnitudes of subsequent ight changes in each iteration are very small, so that many iterations are required to compensate for the increased error in some components in the initial iterations. Our approach is to use a modular network architecture, reducing a K-class problem to a set of K two-class problems, with a separately trained network for each of the simpler problems. Speedups of one order of magnitude have been obtained experimentally, and in some cases convergence was possible using the modular approach but not using a nonmodular network. >
['Rangachari Anand', 'Kishan Mehrotra', 'Chilukuri K. Mohan', 'Sanjay Ranka']
I am writing a research paper on a idea Efficient classification for multiclass problems using modular neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Efficient classification for multiclass problems using modular neural networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The rate of convergence of net output error is very low when training feedforward neural networks for multiclass problems using the backpropagation algorithm. While backpropagation will reduce the Euclidean distance beten the actual and desired output vectors, the differences beten some of the components of these vectors increase in the first iteration. Furthermore, the magnitudes of subsequent ight changes in each iteration are very small, so that many iterations are required to compensate for the increased error in some components in the initial iterations. Our approach is to use a modular network architecture, reducing a K-class problem to a set of K two-class problems, with a separately trained network for each of the simpler problems. Speedups of one order of magnitude have been obtained experimentally, and in some cases convergence was possible using the modular approach but not using a nonmodular network. >"}]