GURLS++  2.0.00
C++ Implementation of GURLS Matlab Toolbox
The GURLS Package in C++

GURLS++ is one of the 4 Grand Unified Laest Squares libraries (GURLS, GURLS++, bGURLS, bGURLS++), a set of modular and easy-to-extend software libraries for efficient supervised learning, which can be adopted effortlessly by both non-specialists and machine learning practitioners (http://lcsl.mit.edu/gurls.html).

GURLS++, the C++ implementation of the Matlab toolbox GURLS, provides an object oriented framework to to solve supervised learning problems via different state-of-the-art learning methods based on Regularized Least Squares. It takes advantage of some favorable properties of regularized least squares algorithm. is tailored to deal in particular with multi-category/multi-label problems.

The library comprises useful routines to perform automatic parameter selection, and simple API's for specific learning algorithms (currently only the Recursive RLS API is available but many others will be soon available).

The specification of the desired machine learning experiment in the library is very straightforward. Basically, it is a formal description of a pipeline, i.e. an ordered sequence of steps. Each step identifies an actual learning task, which can belong to a predefined category. The core of the library is a class called GURLScore, which is responsible for processing the sequence of tasks in the proper order and for linking the output of the former task to the input of the subsequent one.

GURLS-design.png
GURLS++ pipeline

A key role is played by the additional "options" structure, which we usually refer to as "opt". It is used to store all configuration parameters required to customize the behaviour of single tasks in the pipeline. Tasks receive configuration parameters from "opt" in read-only mode and - upon termination - the results are appended to the structure by GURLScore in order to make them available to the subsequent tasks. This allows the user to easily skip the execution of some tasks in a pipeline, by simply inserting the desired results directly into the options structure. Currently, we identify six different task categories:

  • automatic training
  • validation
  • test dataset splitting
  • computation of the kernel matrix
  • model selection
  • optimization and training
  • classifier prediction
  • performance assessment.

All tasks in the same category can be interchanged with each other.

Further documentation:

The GURLS libraries have been developed within the context of the IIT@MIT Lab, established between the Massachusetts Institute of Technology (MIT) and the Istituto Italiano di Tecnologia (IIT) to develop novel learning and perception technologies/algorithms for learning, especially in the visual perception domain, that are inspired by the neuroscience of sensory systems and are developed within the rapidly growing theory of computational learning.

Current Development Status

The current version of this C++ library implements all the core functionalities available in GURLS. Check out the main GURLS repository at (https://github.com/CBCL/GURLS) and keep updated for futher developments.

Author:

Matteo Santoro. (Contacts: matteo.santoro@gmail.com)

Copyright Notice:

Copyright (C) 2013, Matteo Santoro and IIT@MIT Lab

All rights reserved.

GURLS++ is released under the following licence:

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

* Neither the name(s) of the copyright holders nor the names of its contributors or of the Massacusetts Institute of Technology or of the Italian Institute of Technology may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

History of revisions

  1. Version 2.0.00, released 2013 January 24th.

  2. Version 1.0.00, released 2012 June 1th. GURLS++ 1.0.00 is the first complete release.

  3. Version 0.1.00, released 2011 October 7th. GURLS++ 0.1.00 is an initial release and should be used with caution. The library has already been in use, but not every function/method has been thoroughly tested. Only a limited subset of GURLS is interfaced. Commens, bugfixes, enhancements, suggestions are wellcome.

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