转导学习的新规则化算法New Regularized Algorithms for Transducitve Learning |
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课程网址: | http://videolectures.net/ecmlpkdd09_talukdar_nratl/ |
主讲教师: | Partha Pratim Talukdar |
开课单位: | 印度班加罗尔理工学院 |
开课时间: | 2009-10-20 |
课程语种: | 英语 |
中文简介: | 我们提出了一种新的基于图形的标签传播算法用于转导学习。每个示例与无向图中的顶点相关联,并且两个顶点之间的加权边表示两个对应示例之间的相似性。我们建立在最近提出的算法Adsorption上并分析其属性。然后,我们将学习算法表示为多标签分配的凸优化问题,并导出一种有效的算法来解决这个问题。我们陈述了保证算法收敛的条件。我们提供了各种现实世界数据集的实验证据,证明了我们的算法相对于其他算法的有效性。我们还展示了我们的算法可以扩展到包含额外的先验信息,并通过对标签不相互排斥的数据进行分类来演示它。 |
课程简介: | We propose a new graph-based label propagation algorithm for transductive learning. Each example is associated with a vertex in an undirected graph and a weighted edge between two vertices represents similarity between the two corresponding example. We build on Adsorption, a recently proposed algorithm and analyze its properties. We then state our learning algorithm as a convex optimization problem over multi-label assignments and derive an efficient algorithm to solve this problem. We state the conditions under which our algorithm is guaranteed to converge. We provide experimental evidence on various real-world datasets demonstrating the effectiveness of our algorithm over other algorithms for such problems. We also show that our algorithm can be extended to incorporate additional prior information, and demonstrate it with classifying data where the labels are not mutually exclusive. |
关 键 词: | 标签传播算法; 加权边; 凸优化问题 |
课程来源: | 视频讲座网 |
最后编审: | 2019-03-27:lxf |
阅读次数: | 69 |