图分类的概率推理Probabilistic Inference for Graph Classification |
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课程网址: | http://videolectures.net/pmsb06_tsuda_pigc/ |
主讲教师: | Koji Tsuda |
开课单位: | 马克斯普朗克研究所 |
开课时间: | 2007-02-25 |
课程语种: | 英语 |
中文简介: | 图形数据在例如生物信息学和文本处理中越来越流行。图形数据处理的主要缺点在于图形的内在高维度,即,当图形被表示为所有可能子图的指示符的二元特征向量时,维度对于通常的统计方法而言太大。 |
课程简介: | Graph data is getting increasingly popular in, e.g., bioinfor- matics and text processing. A main dificulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary feature vector of indicators of all possible sub- graphs, the dimensionality gets too large for usual statistical methods. |
关 键 词: | 统计学习; 机器学习; 结构化数据 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-27:zyk |
阅读次数: | 33 |