and the synaptic weights of the final network is the weight applied to each expert. This is known as a linear combination of experts. It can be seen that most forms of neural networks are some subset of a linear combination: the standard neural net (where only one expert is used) is simply a linear combination with all and one . A raw average is where all are equal to some constant value, namely one over the total number of experts. A more recent ensemble averaging method is negative correlation learning, proposed by Y. Liu and X. Yao. Now this method has been widely used in evolutionary computing. Benefits The resulting committee is almost always less complex than a single network that would achieve the same level of performance The resulting committee can be trained more easily on smaller input sets The resulting committee often has improved performance over any single network The risk of overfitting is lessened, as there are fewer parameters (weights) which need to be set See also Ensemble learning References Further reading Artificial intelligence