[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:
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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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