Cooperation of neural nets and task decomposition
暂无分享,去创建一个
In order to solve classification problems when using neural nets, it may be necessary to decompose the global task into a few subtasks, each being processed by one specialized network or module. The task is thus performed through a cooperation between all the modules. Such a decomposition may help to find a more efficient solution. The authors describe experiments on cooperating nets, in this case a task that can be totally decomposed by the user using a priori knowledge of the problem. The example considered clearly shows that building an architecture made of independent specialized units is not the most efficient way to perform successive processings of the data. Important improvements, both in classification and speed performances, may be obtained by training the different modules together. Another important point is that this modularity is the only way to build architectures which are suitable for complex tasks.<<ETX>>
[1] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[2] Sylvie Thiria,et al. Cooperation of neural nets for robust classification , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[3] Geoffrey E. Hinton,et al. Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..
[4] Michael I. Jordan,et al. Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks , 1990, Cogn. Sci..