Name
Abstract | ||
|---|---|---|
We compare the performance of multilayer perceptrons (MLPs) obtained using back propagation (BP), decision boundary making (DBM) algorithm and extreme learning machine (ELM), and investigate better method for developing aware agents (A-agent) that are suitable for implementation in portable/wearable computing devices (PIWCD). The DBM has been proposed by us for inducing compact and high performance learning models that are suitable for implementation in P/WCD. The basic idea of the DBM is to generate data to fit the decision boundary (DB) of a high performance model, and then induce a compact model based on the generated data. In our study, support vector machine (SVM) is used as the high performance model, and a single hidden layer MLP is used as the compact model for the DBM algorithm. ELM is paid attention as new learning method for neural networks in recent years. It is known that hidden layer is not to be tuned and available fast training compared to traditional gradient-based learning methods. Experimental results show that the performance of DBM is the highest in three training methods when the number of hidden neurons is small for all databases used in the experiment. This means that the accuracy of DBM converged to high score, when the number of hidden neuron is small. Therefore, we found that DBM is the best algorithm for developing compact and high performance A-agents. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | Extreme Learning Machine, Neural Network, Decision Boundary Making, Aware Agents |
Field | DocType | ISSN |
Data modeling,Extreme learning machine,Computer science,Support vector machine,Artificial intelligence,Artificial neural network,Backpropagation,Decision boundary,Perceptron,dBm,Machine learning | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masato Hashimoto | 1 | 0 | 1.01 |
| Yuya Kaneda | 2 | 5 | 2.70 |
| Qiangfu Zhao | 3 | 214 | 62.36 |
| Yong Liu | 4 | 2526 | 265.08 |