How to Install and Uninstall libshogun-dbg Package on Ubuntu 16.04 LTS (Xenial Xerus)

Last updated: December 25,2024

1. Install "libshogun-dbg" package

Learn how to install libshogun-dbg on Ubuntu 16.04 LTS (Xenial Xerus)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libshogun-dbg
            
                    
    
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2. Uninstall "libshogun-dbg" package

Please follow the guidelines below to uninstall libshogun-dbg on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
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                sudo apt remove
            
                        
                libshogun-dbg
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the libshogun-dbg package on Ubuntu 16.04 LTS (Xenial Xerus)

Package: libshogun-dbg
Priority: extra
Section: universe/debug
Installed-Size: 49564
Maintainer: Ubuntu Developers
Original-Maintainer: Soeren Sonnenburg
Architecture: amd64
Source: shogun
Version: 3.2.0-7.3build4
Replaces: shogun-dbg
Depends: libshogun16 (= 3.2.0-7.3build4)
Breaks: shogun-dbg
Filename: pool/universe/s/shogun/libshogun-dbg_3.2.0-7.3build4_amd64.deb
Size: 47131946
MD5sum: c99183e9e219407b0dffb8780ea44009
SHA1: 514e80e012eb44cdc632e0aaa10245497bec66c8
SHA256: 4413bb884babf7488fa0e868ed5576fb2ec406b8b861c6aedfbcab466a5c91e8
Description-en: Large Scale Machine Learning Toolbox
SHOGUN - is a new machine learning toolbox with focus on large scale kernel
methods and especially on Support Vector Machines (SVM) with focus to
bioinformatics. It provides a generic SVM object interfacing to several
different SVM implementations. Each of the SVMs can be combined with a variety
of the many kernels implemented. It can deal with weighted linear combination
of a number of sub-kernels, each of which not necessarily working on the same
domain, where an optimal sub-kernel weighting can be learned using Multiple
Kernel Learning. Apart from SVM 2-class classification and regression
problems, a number of linear methods like Linear Discriminant Analysis (LDA),
Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to
train hidden markov models are implemented. The input feature-objects can be
dense, sparse or strings and of type int/short/double/char and can be
converted into different feature types. Chains of preprocessors (e.g.
substracting the mean) can be attached to each feature object allowing for
on-the-fly pre-processing.
.
SHOGUN comes in different flavours, a stand-a-lone version and also with
interfaces to Matlab(tm), R, Octave, Readline and Python. This package
contains debug symbols for all interfaces.
Description-md5: c7102983b8576cbbe6c93467d8eb1daf
Homepage: http://www.shogun-toolbox.org
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu