[mlpack-svn] [MLPACK] #201: bibtex citations in documentation, or regular citations?

MLPACK Trac trac at coffeetalk-1.cc.gatech.edu
Fri Feb 3 18:13:42 EST 2012


#201: bibtex citations in documentation, or regular citations?
----------------------+-----------------------------------------------------
 Reporter:  rcurtin   |        Owner:                               
     Type:  wishlist  |       Status:  new                          
 Priority:  trivial   |    Milestone:  mlpack 1.0.1                 
Component:  mlpack    |     Keywords:  citation bibtex documentation
 Blocking:            |   Blocked By:                               
----------------------+-----------------------------------------------------
 Right now I do:

 {{{
 $ nca -h
 Neighborhood Components Analysis (NCA)

   This program implements Neighborhood Components Analysis, both a linear
   dimensionality reduction technique and a distance learning technique.
 The
   method seeks to improve k-nearest-neighbor classification on a dataset
 by
   scaling the dimensions.  The method is nonparametric, and does not
 require a
   value of k.  It works by using stochastic ("soft") neighbor assignments
 and
   using optimization techniques over the gradient of the accuracy of the
   neighbor assignments.

   For more details, see the following published paper:

   @inproceedings{
     author = {Goldberger, Jacob and Roweis, Sam and Hinton, Geoff and
         Salakhutdinov, Ruslan},
     booktitle = {Advances in Neural Information Processing Systems 17},
     pages = {513--520},
     publisher = {MIT Press},
     title = {{Neighbourhood Components Analysis}},
     year = {2004}
   }

   To work, this algorithm needs labeled data.  It can be given as the last
 row
   of the input dataset (--input_file), or alternatively in a separate file
   (--labels_file).

   ...
 }}}

 Should it output the bibtex citation code like that, or should we output
 the actual citation? (a la below)

 {{{
 $ nca -h
 Neighborhood Components Analysis (NCA)

   This program implements Neighborhood Components Analysis, both a linear
   dimensionality reduction technique and a distance learning technique.
 The
   method seeks to improve k-nearest-neighbor classification on a dataset
 by
   scaling the dimensions.  The method is nonparametric, and does not
 require a
   value of k.  It works by using stochastic ("soft") neighbor assignments
 and
   using optimization techniques over the gradient of the accuracy of the
   neighbor assignments.

   For more details, see the following published paper:

   Goldberger, J., Roweis, S., Hinton, G., and Salakhutdinov, R.
 "Neighbourhood
   Components Analysis", pp. 513-520, Advances in Neural Information
 Processing Systems
   17.  MIT Press, 2004.

   To work, this algorithm needs labeled data.  It can be given as the last
 row
   of the input dataset (--input_file), or alternatively in a separate file
   (--labels_file).

   ...
 }}}

 CCing the usual suspects so we can gather ideas.  Don't feel obligated to
 have an opinion. :)

-- 
Ticket URL: <https://trac.research.cc.gatech.edu/fastlab/ticket/201>
MLPACK <www.fast-lab.org>
MLPACK is an intuitive, fast, and scalable C++ machine learning library developed by the FASTLAB at Georgia Tech under Dr. Alex Gray.


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