图 书 馆
非线性组合数据源用于肾细胞癌的细胞核分类Combining Data Sources Nonlinearly for Cell Nucleus Classification of RCell Carcinomenal a |
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| 课程网址: | http://videolectures.net/simbad2011_gonen_carcinoma/ |
| 主讲教师: | Mehmet Gönen |
| 开课单位: | 阿尔托大学 |
| 开课时间: | 2011-10-17 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In kernel-based machine learning algorithms, we can learn a combination of different kernel functions in order to obtain a similarity measure that better matches the underlying problem instead of using a single fixed kernel function. This approach is called multiple kernel learning (MKL). In this paper, we formulate a nonlinear MKL variant and apply it for nuclei classification in tissue microarray images of renal cell carcinoma (RCC). The proposed variant is tested on several feature representations extracted from the automatically segmented nuclei. We compare our results with single-kernel support vector machines trained on each feature representation separately and three linear MKL algorithms from the literature. We demonstrate that our variant obtains more accurate classifiers than competing algorithms for RCC detection by combining information from different feature representations nonlinearly. |
| 关 键 词: | 机器学习; 内核函数; 多内核学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-21：cwx |
| 阅读次数: | 36 |