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学习协调:一种统计方法

Learning to align: a statistical approach
课程网址: http://videolectures.net/ida07_ricci_lta/  
主讲教师: Elisa Ricci
开课单位: 佩鲁贾大学
开课时间: 2007-10-08
课程语种: 英语
中文简介:
我们提出了一种新的反向参数序列比对问题的机器学习方法:给出一组正确的成对全局比对作为训练样例,找到使这些比对最佳的参数值。我们考虑所有不正确比对的分数分布,然后我们搜索那些给定比对的得分尽可能远离这个平均值的参数,用标准偏差的数量来衡量。该归一化距离在统计学中称为“Z得分”。我们证明Z得分是参数的函数,并且可以使用类似于Needleman Wunsch算法的有效动态程序来计算。我们还表明,最大化Z得分归结为简单的二次规划。实验结果证明了该方法的有效性。
课程简介: We present a new machine learning approach to the inverse parametric sequence alignment problem: given as training examples a set of correct pairwise global alignments, find the parameter values that make these alignments optimal.We consider the distribution of the scores of all incorrect alignments, then we search for those parameters for which the score of the given alignments is as far as possible from this mean, measured in number of standard deviations. This normalized distance is called the ‘Z-score’ in statistics. We show that the Z-score is a function of the parameters and can be computed with efficient dynamic programs similar to the Needleman-Wunsch algorithm.We also show that maximizing the Z-score boils down to a simple quadratic program. Experimental results demonstrate the effectiveness of the proposed approach.
关 键 词: 参数序列; 机器学习; 归一化距离
课程来源: 视频讲座网
最后编审: 2019-04-27:cwx
阅读次数: 50