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A statistical learning framework for data mining of large-scale systems : algorithms, implementation, and applications

机译:用于大规模系统数据挖掘的统计学习框架:算法,实现和应用程序

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摘要

A machine learning framework is presented that supports data mining and statistical modeling of systems that are monitored by large-scale sensor networks. The proposed algorithm is novel in that it takes both observations and domain knowledge into consideration and provides a mechanism that combines analytical modeling and inductive learning. An efficient solver is presented that allow the algorithm to solve large-scale problems efficiently. The solver uses a randomized kernel that incorporates domain knowledge into support vector machine learning. It also takes advantage of the sparseness of support vectors and this allows for parallelization and online training to further speed-up of the computation. The solver can be integrated into existing systems, embedded into databases, or exposed as a web service. Understanding the data generated by large-scale system presents several problems. First, statistical modeling approaches may either under-fit or over-fit the data and are sensitive to data quality. Second, learning is a computational extensive process and often becomes intractable when the sample size exceeds several thousands.
机译:提出了一种机器学习框架,该框架支持由大规模传感器网络监视的系统的数据挖掘和统计建模。所提出的算法是新颖的,因为它同时考虑了观测和领域知识,并提供了一种将分析建模和归纳学习相结合的机制。提出了一种有效的求解器,该算法使算法可以有效地解决大规模问题。求解器使用将领域知识纳入支持向量机学习的随机内核。它还利用了支持向量的稀疏性,这允许并行化和在线训练,以进一步加快计算速度。求解器可以集成到现有系统中,嵌入到数据库中或作为Web服务公开。了解大型系统生成的数据会带来一些问题。首先,统计建模方法可能会拟合数据不足或过度拟合,并且对数据质量敏感。其次,学习是一个计算量大的过程,当样本数量超过数千时,学习常常变得很棘手。

著录项

  • 作者

    Tsou Ching-Huei 1973-;

  • 作者单位
  • 年度 2007
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
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