Name
Abstract | ||
|---|---|---|
In this paper, we propose a method for generating guide data, and investigate its efficiency and efficacy for on-line learning with guide data. On-line learning in this research updates a learning model initialized by the decision boundary making algorithm proposed by us in our earlier study. The problem is that, if the guide data are not properly generated, on-line learning may require high computational cost in terms of time, and the learning process may not converge to good result. To solve this problem, we propose to use k-means to cluster all candidates of guide data, and use one datum from each cluster as the guide datum. We conducted experiments on several public databases, using different settings, and confirmed the performance of the proposed method. Specifically, if k=5, we can obtain good models with low computational cost through on-line learning. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | Decision Boundary Making, Multilayer Perceptron, Backpropagation Algorithm, On-Line Learning |
Field | DocType | ISSN |
Data mining,Online machine learning,Algorithmic learning theory,Semi-supervised learning,Instance-based learning,Stability (learning theory),Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Computational learning theory,Machine learning | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuya Kaneda | 1 | 5 | 2.70 |
| Qiangfu Zhao | 2 | 214 | 62.36 |
| Yong Liu | 3 | 2526 | 265.08 |