Name
Abstract | ||
|---|---|---|
As we are usually confronted with a large instance space for real-word data sets, it is significant to develop a useful and efficient multiple-instance learning (MIL) algorithm. MIL, where training data are prepared in the form of labeled bags rather than labeled instances, is a variant of supervised learning. This paper presents a novel MIL algorithm for an extreme learning machine called MI-ELM. A radial basis kernel extreme learning machine is adapted to approach the MIL problem using Hausdorff distance to measure the distance between the bags. The clusters in the hidden layer are composed of bags that are randomly generated. Because we do not need to tune the parameters for the hidden layer, MI-ELM can learn very fast. The experimental results on classifications and multiple-instance regression data sets demonstrate that the MI-ELM is useful and efficient as compared to the state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1515/jisys-2015-0011 | JOURNAL OF INTELLIGENT SYSTEMS |
Keywords | Field | DocType |
Single hidden neural network,multiple-instance learning,extreme learning machine,RBF kernel | Online machine learning,Instance-based learning,Semi-supervised learning,Radial basis function kernel,Active learning (machine learning),Computer science,Extreme learning machine,Polynomial kernel,Unsupervised learning,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
26 | 1 | 0334-1860 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Wang | 1 | 271 | 53.08 |
| Liangjian Cai | 2 | 3 | 0.74 |
| Xin Zhao | 3 | 139 | 17.21 |