Abstract

In this paper, we describe a new probabilistic algorithm for calculating hypotheses as the results of similarities between training examples for a machine learning problem based on a binary similarity operation. Unlike previously proposed probabilistic algorithms, the order of accounting for training examples is fixed for all hypotheses. This algorithm is useful for implementation using a GPGPU. The main result of this paper is the independence of the order of the appearance of training examples of the probabilities of each similarity in the sample.

中文翻译：

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一种计算相似度的概率算法

摘要

在本文中，我们描述了一种新的概率算法，该算法用于根据基于二进制相似度运算的机器学习问题的训练示例之间的相似度结果来计算假设。与先前提出的概率算法不同，对于所有假设而言，训练示例的核算顺序都是固定的。该算法对于使用GPGPU的实现很有用。本文的主要结果是样本中每个相似性概率的训练示例出现顺序的独立性。