An examination of on-line machine learning approaches for pseudo-random generated data

作者: Jia Zhu , Chuanhua Xu , Zhixu Li , Gabriel Fung , Xueqin Lin

DOI: 10.1007/S10586-016-0586-5

关键词:

摘要: A pseudo-random generator is an algorithm to generate a sequence of objects determined by truly random seed which not random. It has been widely used in many applications, such as cryptography and simulations. In this article, we examine current popular machine learning algorithms with various on-line for generated data order find out approach more suitable kind prediction based on algorithms. To further improve the performance, propose novel sample weighted that takes generalization errors each iteration into account. We perform intensive evaluation real Baccarat Casino machines number Java program, are two typical examples data. The experimental results show support vector k-nearest neighbors have better performance than others without set.

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