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基于机器学习方法的系统辨识

System Identification Using Machine Learning Methods
课程网址: http://videolectures.net/bbci2012_wichmann_system_identification/  
主讲教师: Felix A. Wichmann
开课单位: 马克斯普朗克研究所
开课时间: 2012-11-03
课程语种: 英语
中文简介:
在行为层面上理解感知和潜在的认知过程需要一个特征识别问题的解决方案:哪些特征是感官系统计算的基础?我们可以用什么技术来识别他们?因此,精神物理学的中心挑战之一就是系统识别:我们需要推断人类观察者在看到或听到时所使用的关键特征或线索。当面对现实世界中复杂的刺激时,视觉或听觉刺激的哪个方面实际上会影响行为?在我的实验室里,我们开发了基于现代机器学习方法的探索性、数据驱动的系统识别技术,从人类行为判断中推断出关键特征。传统的“泡沫”方法和“这些方法”的优点是什么。
课程简介: Understanding perception and the underlying cognitive processes on a behavioral level requires a solution to the feature identification problem: Which are the features on which sensory systems base their computations? What techniques can we use to identify them? Thus one of the central challenges in psychophysics is System Identification: We need to infer the critical features, or cues, human observers make use of when they see or hear. What aspect of the visual or auditory stimulus actually influences behaviour if faced with real-world, complex stimuli? In my laboratory we have developed exploratory, data-driven system identification techniques based on modern machine learning methods to infer the critical features from human behavioural judgments. I will present these methods and show what their benefits are over the traditional “reverse-correlation” approach and the “bubbles technique”.
关 键 词: 机器学习; 感觉系统; 听觉刺激
课程来源: 视频讲座网
数据采集: 2020-12-14:yxd
最后编审: 2021-01-08:yumf
阅读次数: 26