An iterative pruning algorithm for feedforward neural networks
نویسندگان
چکیده
منابع مشابه
An iterative pruning algorithm for feedforward neural networks
The problem of determining the proper size of an artificial neural network is recognized to be crucial, especially for its practical implications in such important issues as learning and generalization. One popular approach for tackling this problem is commonly known as pruning and it consists of training a larger than necessary network and then removing unnecessary weights/nodes. In this paper...
متن کاملA Penalty-Function Approach for Pruning Feedforward Neural Networks
This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network ar...
متن کاملAn iterative learning algorithm for feedforward neural networks with random weights
Feedforward neural networks with random weights (FNNRWs), as random basis function approximators, have received considerable attention due to their potential applications in dealing with large scale datasets. Special characteristics of such a learner model come from weights specification, that is, the input weights and biases are randomly assigned and the output weights can be analytically eval...
متن کاملDistributed learning algorithm for feedforward neural networks
With the appearance of huge data sets new challenges have risen regarding the scalability and efficiency of Machine Learning algorithms, and both distributed computing and randomized algorithms have become effective ways to handle them. Taking advantage of these two approaches, a distributed learning algorithm for two-layer neural networks is proposed. Results demonstrate a similar accuracy whe...
متن کاملA new feedforward neural network hidden layer neuron pruning algorithm
This paper deals with a new approach to detect the structure (i.e. determination of the number of hidden units) of a feedforward neural network (FNN). This approach is based on the principle that any FNN could be represented by a Volterra series such as a nonlinear inputoutput model. The new proposed algorithm is based on the following three steps: first, we develop the nonlinear activation fun...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks
سال: 1997
ISSN: 1045-9227,1941-0093
DOI: 10.1109/72.572092