首页> 外文OA文献 >Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

【2h】

Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

机译：在hpc机上实现网络熵算法及其在高维实验数据中的应用

页面导航

摘要
著录项
相似文献
相关主题

摘要

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets areudalready available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computingudmachine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed.udThe aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.

机译：网络理论是一个多产且活跃的领域，尤其是在它接近生物学时。该理论的新概念可以在已经普遍使用广泛的数据集进行分析的领域中找到应用，而无需投入资金来收集它们。完成分析所需的唯一工具很容易获得：计算机计算机和良好的算法。随着这两个工具的发展，由于技术的进步和人类的努力，可以分析越来越广泛的数据集。 ud本文的目的是双重的。首先，提供对这些概念之一的概述，该概念起源于网络理论与统计力学之间的交汇点：网络集合的熵。在文献中已从不同角度描述了该数量。我们的方法试图综合各种观点。工作的第二部分专门介绍一种并行算法，该算法可以在大量数据集中评估此数量。最终，该算法还将用于分析来自生物学的高通量数据。

著录项

作者
Molari Marco;
展开▼
作者单位

展开▼
年度 2013
总页数
原文格式 PDF
正文语种 en
中图分类

相似文献

外文文献
中文文献
专利

1. Stochastic parallel extreme artificial hydrocarbon networks: An implementation for fast and robust supervised machine learning high-dimensional data [J] . Hiram Ponce, Paulo V. de Campos Souza, Augusto Junio Guimaraes, Engineering Applications of Artificial Intelligence . 2020,第Mara期

机译：随机并行极端人工碳氢化合物网络：一种用于快速，鲁棒的有监督的机器学习高维数据的实现
2. An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms [J] . A. R. Pinto, C. Montez, G. Araujo, Information Fusion . 2014,第Null期

机译：一种使用遗传机器学习算法在无线传感器网络中实现数据融合技术的方法
3. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data [J] . Rainer Opgen-Rhein, Korbinian Strimmer BMC Systems Biology . 2007,第1期

机译：从关联到因果网络：一种简单的近似学习算法及其在高维植物基因表达数据中的应用
4. Inner parameters' optimization in the artificial neural network for the traffic data classification in radiofrequency applications: Classification of nonstationary data using the machine learning algorithm “random forest” [C] . Kalashnikov Evgeniy Alexandrovich, Kondybayeva Almagul Baurzhanovna, Ositis Anastasia Petrovna Systems of Signals Generating and Processing in the Field of on Board Communications . 2018

机译：人工神经网络的内部参数优化，用于射频应用中的交通数据分类：使用机器学习算法“随机森林”对非平稳数据进行分类
5. Fast nonparametric machine learning algorithms for high-dimensional massive data and applications. [D] . Liu, Ting. 2006

机译：用于高维海量数据和应用程序的快速非参数机器学习算法。
6. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data [O] . Rainer Opgen-Rhein, Korbinian Strimmer 2007

机译：从相关到因果网络：一种简单的近似学习算法及其在高维植物基因表达数据中的应用
7. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data [O] . 2007

机译：从相关到因果网络：一种简单的近似学习算法及其在高维植物基因表达数据中的应用
8. Fast Nonparametric Machine Learning Algorithms for High-Dimensional Massive Data and Applications [R] . Liu, T. 2006

机译：高维海量数据及其应用的快速非参数机器学习算法

Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

摘要

著录项

相似文献

相关主题

期刊订阅