Name
Abstract | ||
|---|---|---|
A novel method for learning a linear multilayer feedforward artificial neural network (ANN) by using ensembles of boosted decision stumps is presented. Network parameters are adapted through a layerwise iterative traversal of neurons with weights of each neuron learned by using a boosting based ensemble and an appropriate reduction. Performances of several neural network models using the proposed method are compared for a variety of datasets with networks learned using three other algorithms, namely Perceptron learning rule, gradient decent back propagation algorithm, and Boostron learning. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26532-2_38 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Back propagation algorithm,Gradient descent,Tree traversal,Pattern recognition,Computer science,Learning rule,Boosting (machine learning),Artificial intelligence,Artificial neural network,Perceptron,Feed forward | Conference | 9489 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.45 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mirza M. Baig | 1 | 11 | 1.43 |
| El-Sayed M. El-Alfy | 2 | 187 | 31.43 |
| Mian Awais | 3 | 59 | 11.53 |