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Incremental
Hierarchical
Discriminant Regression is a self-organization engine, which
incrementally builds a decision or regression tree for a high
dimensional input and output space. It operates sample by sample,
and
in real time. IHDR is an approximate
computational
model
for automatic development of the associative cortical areas, with
sensory
inputs and motor (action) outputs. We have
developed the
IHDR algorithm with both C++ and MATLAB. It has been tested with
many
types of data, such as indoor and outdoor robot navigation and face
classification with different poses and lighting conditions. The
experimental results have demonstrated
that the
algorithm can deal with a very large sample sizes as well as very large
input dimension, and still run in real-time.
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IHDR
Download: C++ Library |
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Please download the zip file here, which provides a library of the IHDR tree, named as ”libihdr.a”. The makefile is used to generate an executable file – ihdrexample, in order to to provide an example of how to use the library.
– library: “libihdr.a” – header files: “IHDR.h, Cgs.h, Mah dist.h, Misc.h, Node.h, Param.h, Prob.h, sample.h, Transfer.h, Tree.h” – makefile: “makefile” – example file: “ihdrexample.cpp” – training file: “letter.tra” – testing file: “letter.tes” – readme file of the “letter” dataset: “letter doc.txt” The example file shows you how to use “libihdr.a.”. The task here is to generate an IHDR tree for a classification task, based on the training file and testing file specified above. Run the makefile to generate the executable file “ihdrexample.” The format to run the executable file is “ihdrexample -x <input dimension> -y <output dimension> -n <name of the tree>”. According to the example, you can also generate your own program for other classification or regression tasks. The package contains the source code for the IHDR tree, which can be used for further modification and development. |