Name
Abstract | ||
|---|---|---|
A dynamic classification using the support vector machine (SVM) technique is presented in this paper as a new 'incremental' framework for multiple-classifying video stream data. The contribution of this study is the derivation of a unique, fast and simple to implement technique that allows multi-classification of behavioral motions based on an adaptation of the least-square SVM (LS-SVM) formulation. This dynamic approach leads to an extension of SVM beyond its current static image-based learning capabilities. The proposed incremental multi-classification method is applied to video stream data, which consists of an articulated humanoid model monitored by a surveillance camera. The initial supervised off-line learning phase is followed by a visual behavior data acquisition and then an incremental learning phase. The resulting error rate and the confidence level for the proposed technique demonstrate its validity and merits in articulated motion learning. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and provides the advantage of reducing both the model training time and the information storage requirements of the overall system which are both essential for dynamic soft computing applications. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.asoc.2007.11.008 | Appl. Soft Comput. |
Keywords | Field | DocType |
incremental support vector machine,support vector machine,behavior learning,online learning,least-square svm,dynamic soft computing,current static image-based learning,dynamic approach,dynamic classification,dynamic soft computing application,multiple classification,initial supervised off-line learning,incremental learning phase,behavior learning.,articulated motion learning,video stream data,least square,error rate,domain knowledge,confidence level,soft computing,data acquisition | Structured support vector machine,Online machine learning,Data mining,Semi-supervised learning,Active learning (machine learning),Computer science,Support vector machine,Artificial intelligence,Relevance vector machine,Computational learning theory,Soft computing,Machine learning | Journal |
Volume | Issue | ISSN |
8 | 4 | Applied Soft Computing Journal |
Citations | PageRank | References |
20 | 0.72 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mariette Awad | 1 | 104 | 21.39 |
| Yuichi Motai | 2 | 230 | 24.68 |