Name
Abstract | ||
|---|---|---|
In general, the similar input data have the similar output target values. A novel Fast Support Vector Regression (FSVR) is proposed on the reduced training set. Firstly, the improved learning machine divides the training data into blocks by using the traditional clustering methods, such as K-mean and FCM clustering techniques. Secondly, the membership function on each block is defined by the corresponding target values of the training data, all the training data have the membership degree falling into the interval [0, 1], which can vary the penalty coefficient by multiplying C. Thirdly, the reduced training set is used to training FSVR, which consists of the data with the membership degrees, which are greater than or equal to the selected suitable parameter ? . The experimental results on the traditional machine learning data sets show that the FSVR can not only achieve the better or acceptable performance but also downsize the number of training data and speed up training. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21524-7_44 | ICSI |
Keywords | Field | DocType |
similar input data,improved learning machine,fcm clustering technique,training fsvr,training data,fast support vector regression,corresponding target value,membership function,reduced training set,membership degree,cut,support vector regression,k means clustering,support vector machines | Training set,Learning machine,k-means clustering,Data set,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Cluster analysis,Membership function,Machine learning,Speedup | Conference |
Volume | ISSN | Citations |
6729 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenyong Zhou | 1 | 0 | 2.37 |
| Yan Xiong | 2 | 0 | 0.34 |
| Chang-an Wu | 3 | 19 | 5.66 |
| Hongbing Liu | 4 | 59 | 8.74 |