Name
Title | ||
|---|---|---|
Multi-dimensional data construction method with its application to learning from small-sample-sets |
Abstract | ||
|---|---|---|
Insufficient training data is one of the major problems in neural network learning, because it leads to poor learning performance. In order to enhance an intelligent learning process, it is necessary to exploit the features of the problem from the available information even with limited scale. Due to the shortcomings of the existing methods for data generation; and also in general, a problem is described by multiple attributes, this study has first extended the developed one-dimensional Data Construction Method (DCM) for virtual data generation to multidimensional continuous space as denoted by m-DCM. Then, sensitivity analysis and numerical illustration have been carried out. By incorporating m-DCM into a supervised neural network learning process, we have shown to overcome the existing unbounded and immeasurable problems and provided a better learning performance in a comparative manner. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3233/IDA-2010-0411 | Intell. Data Anal. |
Keywords | Field | DocType |
virtual data generation,multi-dimensional data construction method,insufficient training data,intelligent learning process,existing method,major problem,poor learning performance,data generation,immeasurable problem,neural network learning,better learning performance,multiple dimensions | Online machine learning,Competitive learning,Data mining,Semi-supervised learning,Instance-based learning,Stability (learning theory),Active learning (machine learning),Computer science,Supervised learning,Unsupervised learning,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
14 | 1 | 1088-467X |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsiao-Fan Wang | 1 | 278 | 27.24 |
| Chun-Jung Huang | 2 | 23 | 3.36 |