Name
Abstract | ||
|---|---|---|
In order to learn a given data set, a learning system often has to learn too much on some data points in the given data set in order to learn well the rest of the given data. Such unnecessary learning might lead to both the higher complexity and overfitting in the learning system. In order to control the complexity of neural network ensembles, difference learning is introduced into negative correlation learning. The idea of difference learning is to let each individual in an ensemble learn to be different to the ensemble on some selected data points when the outputs of the ensemble are too close to the target values of these data points. It has been found that such difference learning could control not only overfitting in an ensemble, but also weakness among the individuals in the ensemble. Experimental results were conducted to show how such difference learning could create rather weak learners in negative correlation learning. |
Year | Venue | Field |
|---|---|---|
2015 | ISICA | Data point,Negative correlation,Computer science,Artificial intelligence,Overfitting,Artificial neural network,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 1 | |