9.520统计学习理论与应用(麻省理工学院)9.520 Statistical Learning Theory and Applications (MIT) |
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课程网址: | http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-520-sta... |
主讲教师: | 信息不详。欢迎您在右侧留言补充。 |
开课单位: | 麻省理工学院 |
开课时间: | 信息不详。欢迎您在右侧留言补充。 |
课程语种: | 英语 |
中文简介: | 从现代统计学习理论的角度着眼于监督学习问题,从稀疏数据的多元函数逼近理论入手。开发基本工具,例如正则化,包括用于回归和分类的支持向量机。使用稳定性和VC理论推导出泛化界限。讨论增强和特征选择等主题。检查几个领域的应用:计算机视觉,计算机图形,文本分类和生物信息学。计划最终项目和实际操作应用程序和练习,与本主题中描述的技术的快速增加的实际用途并行。 |
课程简介: | Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject. |
关 键 词: | 多元函数; 泛化界; 近似稀疏数据 |
课程来源: | 信息不详。欢迎您在右侧留言补充。 |
最后编审: | 2015-10-12:linxl |
阅读次数: | 447 |