| Zhang Xiaoyu ( 张晓宇).[J].高技术通讯(英文),2012,18(2):208~213 |
|
| Dynamic batch selective sampling based on version space analysis① |
| |
| DOI： |
| 中文关键词: |
| 英文关键词: relevance feedback, active learning, selective sampling, support vector machine(SVM), version space |
| 基金项目: |
| Author Name | Affiliation | | Zhang Xiaoyu ( 张晓宇) | |
|
| Hits: 752 |
| Download times: 0 |
| 中文摘要: |
| |
| 英文摘要: |
| A novel dynamic batch selective sampling algorithm based on version space analysis is presented. In the traditional batch selective sampling, example selection is entirely determined by the existing unreliable classification boundary; meanwhile, within a batch, examples labeled previously fail to provide instructive information for the selection of the rest. As a result, using the examples selected in batch mode for model refinement will jeopardize the classification performance. Based on the duality between feature space and parameter space under the SVM active learning framework, dynamic batch selective sampling is proposed to address the problem. We select a batch of examples dynamically, using the examples labeled previously as guidance for further selection. In this way, the selection of feedback examples is determined by both the existing classification model and the examples labeled previously. Encouraging experimental results demonstrate the effectiveness of the proposed algorithm. |
|
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
