从ubuntu software center安装matlab,一直没有安装完成,也没法取消,怎么办?

密函一封 2015-11-01 11:18:31
请看图,很奇怪啊!继续等着吗?进度条一直都没动,感觉安装不上啊!?
想取消掉,该怎么取消呢? 不取消我下边想安装浏览器也安不上。



这是系统详细信息,不知是否有用。
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密函一封 2015-11-13
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2楼回答有帮助,看了详细信息,软件中心提供的确实不是完整的安装包,只是整合已安装好的MATLAB,使其用起来更贴近Debian系统版本
密函一封 2015-11-13
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谢谢大家的回答了…… 最后自己下了安装包
ChampangeYo 2015-11-02
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这个matlab好像不是完整版的。只是提供了安装Matlab的简易方法。你看看英文介绍。
尽头2nxszn98 2015-11-01
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话说我很少用 ubuntu 的软件中心,一般都是在网上下载好资源,然后手动安装
MSR Identity Toolbox: A Matlab Toolbox for Speaker Recognition Research Version 1.0 Seyed Omid Sadjadi, Malcolm Slaney, and Larry Heck Microsoft Research, Conversational Systems Research Center (CSRC) s.omid.sadjadi@gmail.com, {mslaney,larry.heck}@microsoft.com This report serves as a user manual for the tools available in the Microsoft Research (MSR) Identity Toolbox. This toolbox contains a collection of Matlab tools and routines that can be used for research and development in speaker recognition. It provides researchers with a test bed for developing new front-end and back-end techniques, allowing replicable evaluation of new advancements. It will also help newcomers in the field by lowering the “barrier to entry”, enabling them to quickly build baseline systems for their experiments. Although the focus of this toolbox is on speaker recognition, it can also be used for other speech related applications such as language, dialect and accent identification. In recent years, the design of robust and effective speaker recognition algorithms has attracted significant research effort from academic and commercial institutions. Speaker recognition has evolved substantially over the past 40 years; from discrete vector quantization (VQ) based systems to adapted Gaussian mixture model (GMM) solutions, and more recently to factor analysis based Eigenvoice (i-vector) frameworks. The Identity Toolbox provides tools that implement both the conventional GMM-UBM and state-of-the-art i-vector based speaker recognition strategies. A speaker recognition system includes two primary components: a front-end and a back-end. The front-end transforms acoustic waveforms into more compact and less redundant representations called acoustic features. Cepstral features are most often used for speaker recognition. It is practical to only retain the high signal-to-noise ratio (SNR) regions of the waveform, therefore there is also a need for a speech activity detector (SAD) in the fr

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