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一个有界的projectron:基于核感知器

The Projectron: a Bounded Kernel-Based Perceptron
课程网址: http://videolectures.net/icml08_orabona_pbk/  
主讲教师: Francesco Orabona
开课单位: 芝加哥丰田技术学院
开课时间: 2008-08-04
课程语种: 英语
中文简介:
我们提出了一种具有有限内存增长的判别式在线算法,该算法基于基于内核的Perceptron。通常,用于存储在线假设的基于内核的Perceptron所需的存储器不受限制。以前的工作一直专注于丢弃部分实例,以保持内存的界限。在所提出的算法中,实例不被丢弃,而是投射到先前在线假设所跨越的空间上。我们得出一个相对错误的界限,并将我们的算法在分析和经验上与最先进的Forgetron算法进行比较(Dekel等,2007)。我们的算法的第一个变体,称为Projectron,优于Forgetron。第二种变体称为Projectron ++,甚至优于Perceptron。
课程简介: We present a discriminative online algorithm with a bounded memory growth, which is based on the kernel-based Perceptron. Generally, the required memory of the kernel-based Perceptron for storing the online hypothesis is not bounded. Previous work has been focused on discarding part of the instances in order to keep the memory bounded. In the proposed algorithm the instances are not discarded, but projected onto the space spanned by the previous online hypothesis. We derive a relative mistake bound and compare our algorithm both analytically and empirically to the state-of-the-art Forgetron algorithm (Dekel et al, 2007). The first variant of our algorithm, called Projectron, outperforms the Forgetron. The second variant, called Projectron++, outperforms even the Perceptron.
关 键 词: 核感知器; 算法分析; Forgetron算法
课程来源: 视频讲座网
最后编审: 2020-06-29:yumf
阅读次数: 93