[mlpack-git] (blog) master: 4th week. (463298c)

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Repository : https://github.com/mlpack/blog
On branch  : master
Link       : https://github.com/mlpack/blog/compare/13e13adba487ce4fa3a63137577b574493f21028...463298cf4ef249f540f850eae44ec6013e4a55f8

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commit 463298cf4ef249f540f850eae44ec6013e4a55f8
Author: MarcosPividori <marcos.pividori at gmail.com>
Date:   Tue Jun 21 00:09:33 2016 -0300

    4th week.


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+Title: Approximate Nearest Neighbor Search - Week 4
+Date: 2016-06-20 21:00:00
+Tags: gsoc, knn, kfn
+Author: Marcos Pividori
+
+Last week, I completed the improvement of existing code for NSModel
+[[1]](http://github.com/mlpack/mlpack/issues/674), using boost variant to manage
+different options for tree types.
+
+The general functionalities were implemented through visitor classes, taking
+advantage of template specialization to differentiate those tree types that
+accept leafSize as a parameter, where we need a slightly different procedure.
+
+AppVeyor has shown some problems with the MSVC compiler, in the resolution of
+template parameters of alias templates, similar to an old issue:
+[[2]](https://github.com/mlpack/mlpack/issues/476)
+We finally managed to fix this including some extra definitions.
+
+All this work was merged in the PR:
+[[3]](https://github.com/mlpack/mlpack/pull/693).
+
+Also, I have been updating the benchmarking system to include an epsilon
+parameter for neighbor search, not only with mlpack, but also with other
+libraries like [ANN](https://www.cs.umd.edu/~mount/ANN/) and
+[FLANN](http://www.cs.ubc.ca/research/flann/).
+
+Now, I am working in adding a new view to the benchmarking system, to plot the
+progress of a specific metric for different values of a method parameter. In
+particular, we are interesting in analyzing the number of base cases and runtime
+for different values of approximation error (epsilon). A trade-off between doing
+neighbor search accurately and doing it quickly.
+
+Next week, I plan to finish this comparison and start working in the
+implementation of some alternative approaches like Spill Trees
+[[4]](http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2005_187.pdf).
+