Information Fusion of Large Number of Sources with Support Vector Machine Techniques

机译：支持向量机技术在大量信息源融合中的应用

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Applications of information fusion include cases that involve a large number of information sources. Methods developed in the context of few information sources may not, and often do not, scale well to cases involving a large number of sources. This paper addresses specifically the problem of information fusion of large number of information sources. Performance of Support Vector Machine (SVM) based approach is investigated in input spaces consisting of thousands of information sources. Microarray pattern recognition, an important bioinformatics task with significant medical diagnostics applications, is considered from the information and sensor data fusion viewpoint, and recognition performance experiments conducted on microarray data are discussed. An approach involving high-dimensional input space partitioning is presented and its efficacy is investigated. The aspects of feature-level and decision-level fusion are discussed as well. The results indicate the feasibility of the SVM based information fusion with large number of information sources.

机译：信息融合的应用包括涉及大量信息源的案例。在少数信息源的背景下开发的方法可能（而且通常不会）很好地适用于涉及大量信息源的案例。本文专门针对大量信息源的信息融合问题。在包含数千个信息源的输入空间中研究了基于支持向量机（SVM）的性能。从信息和传感器数据融合的角度考虑了微阵列模式识别是一项重要的生物信息学任务，具有重要的医学诊断应用程序，并讨论了对微阵列数据进行的识别性能实验。提出了一种涉及高维输入空间划分的方法，并研究了其有效性。还讨论了特征级和决策级融合方面。结果表明基于支持向量机的信息融合与大量信息源的可行性。

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《Conference on Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003 Apr 23-25, 2003 Orlando, Florida, USA》|2003年|p.13-23|共11页
会议地点 Orlando FL(US)
作者
Jerome J. Braun; Sunil P. Jeswani;
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作者单位

Lincoln Laboratory, Massachusetts Institute of Technology 244 Wood Street, Lexington, MA 02420-9185, USA;

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会议组织
原文格式 PDF
正文语种 eng
中图分类自动化技术、计算机技术;
关键词
information fusion; multisensor data fusion; bioinformatics; microarrays; support vector machine;

机译：信息融合；多传感器数据融合；生物信息学；微阵列；支持向量机;

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Information Fusion of Large Number of Sources with Support Vector Machine Techniques

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