A New Learning Scheme for Neural Network Ensembles
نویسندگان
چکیده
We propose a new method for training an ensemble of neural networks. A population of networks is created and maintained such that more probable networks replicate and less probable networks vanish. Each individual network is updated using random weight changes. This produces a diversity among the networks which is important for the ensemble prediction using the population. The method is compared against Bayesian learning for neural networks, Bagging and a simple neural network ensemble, on three datasets. The results show that the population method can be used as an efficient neural network learning algorithm.
منابع مشابه
Learning Curve Consideration in Makespan Computation Using Artificial Neural Network Approach
This paper presents an alternative method using artificial neural network (ANN) to develop a scheduling scheme which is used to determine the makespan or cycle time of a group of jobs going through a series of stages or workstations. The common conventional method uses mathematical programming techniques and presented in Gantt charts forms. The contribution of this paper is in three fold. First...
متن کاملDesigning Kernel Scheme for Classifiers Fusion
In this paper, we propose a special fusion method for combining ensembles of base classifiers utilizing new neural networks in order to improve overall efficiency of classification. While ensembles are designed such that each classifier is trained independently while the decision fusion is performed as a final procedure, in this method, we would be interested in making the fusion process more a...
متن کاملUtilizing a new feed-back fuzzy neural network for solving a system of fuzzy equations
This paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. Our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. This architecture of articial neural networks, can get a real input vector and calculates its corresponding fuzzy o...
متن کاملNeural Network Ensembles, Cross Validation, and Active Learning
Learning of continuous valued functions using neural network ensembles (committees) can give improved accuracy, reliable estimation of the generalization error, and active learning. The ambiguity is defined as the variation of the output of ensemble members averaged over unlabeled data, so it quantifies the disagreement among the networks. It is discussed how to use the ambiguity in combination...
متن کاملMaintaining Population Diversity By Minimizing Mutual Information
Based on negative correlation learning [1] and evolutionary learning, evolutionary ensembles with negative correlation learning (EENCL) was proposed for learning and designing of neural network ensembles [2]. The idea of EENCL is to regard the population of neural networks as an ensemble, and the evolutionary process as the design of neural network ensembles. EENCL used a tness sharing based on...
متن کامل