数据挖掘使用/spl Mscr//spl Lscr//spl Cscr/++一个机器学习的c++库

Ron Kohavi, D. Sommerfield, James Dougherty
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引用次数: 29

摘要

包括机器学习、统计分析和模式识别技术在内的数据挖掘算法可以极大地提高我们对数据仓库的理解,这些数据仓库现在变得越来越普遍。本文重点介绍了分类算法,并回顾了对多种分类算法的需求。我们描述了一个名为/spl Mscr//spl Lscr//spl Cscr/++的系统,该系统旨在通过比较不同算法在特定感兴趣的数据集上的效用,帮助为给定数据集选择合适的分类算法。/spl Mscr//spl Lscr//spl Cscr/++不仅为这种比较提供了一个工作平台,而且还提供了一个c++类库来帮助开发新算法,特别是混合算法和多策略算法。这样的算法通常很难从头开始编写。我们讨论了设计问题、与其他程序的接口以及结果分类器的可视化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data mining using /spl Mscr//spl Lscr//spl Cscr/++ a machine learning library in C++
Data mining algorithms including machine learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper, we focus on classification algorithms and review the need for multiple classification algorithms. We describe a system called /spl Mscr//spl Lscr//spl Cscr/++ which was designed to help choose the appropriate classification algorithm for a given dataset by making it easy to compare the utility of different algorithms on a specific dataset of interest. /spl Mscr//spl Lscr//spl Cscr/++ not only provides a work-bench for such comparisons, but also provides a library of C++ classes to aid in the development of new algorithms, especially hybrid algorithms and multi-strategy algorithms. Such algorithms are generally hard to code from scratch. We discuss design issues, interfaces to other programs, and visualization of the resulting classifiers.
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