On the Integration of Machine Learning and Array Databases

Sebastián Villarroya, P. Baumann
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引用次数: 12

Abstract

Machine Learning is increasingly being applied to many different application domains. From cancer detection to weather forecast, a large number of different applications leverage machine learning algorithms to get faster and more accurate results over huge datasets. Although many of these datasets are mainly composed of array data, a vast majority of machine learning applications do not use array databases. This tutorial focuses on the integration of machine learning algorithms and array databases. By implementing machine learning algorithms in array databases users can boost the native efficient array data processing with machine learning methods to perform accurate and fast array data analytics.
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论机器学习与阵列数据库的集成
机器学习越来越多地应用于许多不同的应用领域。从癌症检测到天气预报,大量不同的应用程序利用机器学习算法在庞大的数据集上获得更快、更准确的结果。尽管其中许多数据集主要由数组数据组成,但绝大多数机器学习应用程序并不使用数组数据库。本教程的重点是机器学习算法和数组数据库的集成。通过在阵列数据库中实现机器学习算法,用户可以利用机器学习方法提高阵列数据处理的效率,从而实现准确、快速的阵列数据分析。
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