Diferencia entre revisiones de «GIC-source-code-free-libre»

De Grupo de Inteligencia Computacional (GIC)
Sin resumen de edición
 
(No se muestran 95 ediciones intermedias de 8 usuarios)
Línea 1: Línea 1:
== Hyperspectral Imagery Synthesis tools for MATLAB ==
* [[Media:synthesis.zip | Download]] (21.6 Mb)


=== IC Synthetic Hyperspectral Collection ===
==== Legendre Collection ====
{| border="1" cellpadding="10"
! colspan="5"|Parameters
! colspan="5"|Signal-to-Noise Ratio (SNR)
|-
! ne
! size
! min
! max
! Coef
! Without noise
! 20db
! 40db
! 60db
! 80db
|- align="center"
| 2
| 64
| 1
| 10
| 100
| [[Media:2e64x64LegendreDefault.zip | download]]
| [[Media:2e64x64LegendreDefaultSNR20.zip | download]]
| [[Media:2e64x64LegendreDefaultSNR40.zip | download]]
| [[Media:2e64x64LegendreDefaultSNR60.zip | download]]
| [[Media:2e64x64LegendreDefaultSNR80.zip | download]]
|- align="center"
| 2
| 64
| 1
| 5
| 100
| [[Media:2e64x64LegendreMaxOrder5.zip | download]]
| [[Media:2e64x64LegendreMaxOrder5SNR20.zip | download]]
| [[Media:2e64x64LegendreMaxOrder5SNR40.zip | download]]
| [[Media:2e64x64LegendreMaxOrder5SNR60.zip | download]]
| [[Media:2e64x64LegendreMaxOrder5SNR80.zip | download]]
|- align="center"
| 2
| 64
| 1
| 10
| 10
| [[Media:2e64x64LegendreMaxCoef10.zip | download]]
| [[Media:2e64x64LegendreMaxCoef10SNR20.zip | download]]
| [[Media:2e64x64LegendreMaxCoef10SNR40.zip | download]]
| [[Media:2e64x64LegendreMaxCoef10SNR60.zip | download]]
| [[Media:2e64x64LegendreMaxCoef10SNR80.zip | download]]
|- align="center"
| 2
| 64
| 1
| 5
| 10
| [[Media:2e64x64LegendreMaxOrder5MaxCoef10.zip | download]]
| [[Media:2e64x64LegendreMaxOrder5MaxCoef10SNR20.zip | download]]
| [[Media:2e64x64LegendreMaxOrder5MaxCoef10SNR40.zip | download]]
| [[Media:2e64x64LegendreMaxOrder5MaxCoef10SNR60.zip | download]]
| [[Media:2e64x64LegendreMaxOrder5MaxCoef10SNR80.zip | download]]
|- align="center"
| 2
| 128
| 1
| 10
| 100
|
|
|
|
|
|- align="center"
| 2
| 128
| 1
| 5
| 100
|
|
|
|
|
|- align="center"
| 2
| 128
| 1
| 10
| 10
|
|
|
|
|
|- align="center"
| 2
| 128
| 1
| 5
| 10
|
|
|
|
|
|- align="center"
| 2
| 256
| 1
| 10
| 100
|
|
|
|
|
|- align="center"
| 2
| 256
| 1
| 5
| 100
|
|
|
|
|
|- align="center"
| 2
| 256
| 1
| 10
| 10
|
|
|
|
|
|- align="center"
| 2
| 256
| 1
| 5
| 10
|
|
|
|
|
|- align="center"
| 3
| 64
| 1
| 10
| 100
|
|
|
|
|
|- align="center"
| 3
| 64
| 1
| 5
| 100
|
|
|
|
|
|- align="center"
| 3
| 64
| 1
| 10
| 10
|
|
|
|
|
|- align="center"
| 3
| 64
| 1
| 5
| 10
|
|
|
|
|
|- align="center"
| 3
| 128
| 1
| 10
| 100
|
|
|
|
|
|- align="center"
| 3
| 128
| 1
| 5
| 100
|
|
|
|
|
|- align="center"
| 3
| 128
| 1
| 10
| 10
|
|
|
|
|
|- align="center"
| 3
| 128
| 1
| 5
| 10
|
|
|
|
|
|- align="center"
| 3
| 256
| 1
| 10
| 100
|
|
|
|
|
|- align="center"
| 3
| 256
| 1
| 5
| 100
|
|
|
|
|
|- align="center"
| 3
| 256
| 1
| 10
| 10
|
|
|
|
|
|- align="center"
| 3
| 256
| 1
| 5
| 10
|
|
|
|
|
|- align="center"
| 4
| 64
| 1
| 10
| 100
|
|
|
|
|
|- align="center"
| 4
| 64
| 1
| 5
| 100
|
|
|
|
|
|- align="center"
| 4
| 64
| 1
| 10
| 10
|
|
|
|
|
|- align="center"
| 4
| 64
| 1
| 5
| 10
|
|
|
|
|
|- align="center"
| 4
| 128
| 1
| 10
| 100
|
|
|
|
|
|- align="center"
| 4
| 128
| 1
| 5
| 100
|
|
|
|
|
|- align="center"
| 4
| 128
| 1
| 10
| 10
|
|
|
|
|
|- align="center"
| 4
| 128
| 1
| 5
| 10
|
|
|
|
|
|- align="center"
| 4
| 256
| 1
| 10
| 100
|
|
|
|
|
|- align="center"
| 4
| 256
| 1
| 5
| 100
|
|
|
|
|
|- align="center"
| 4
| 256
| 1
| 10
| 10
|
|
|
|
|
|- align="center"
| 4
| 256
| 1
| 5
| 10
|
|
|
|
|
|- align="center"
| 5
| 64
| 1
| 10
| 100
|
|
|
|
|
|- align="center"
| 5
| 64
| 1
| 5
| 100
|
|
|
|
|
|- align="center"
| 5
| 64
| 1
| 10
| 10
|
|
|
|
|
|- align="center"
| 5
| 64
| 1
| 5
| 10
|
|
|
|
|
|- align="center"
| 5
| 128
| 1
| 10
| 100
|
|
|
|
|
|- align="center"
| 5
| 128
| 1
| 5
| 100
|
|
|
|
|
|- align="center"
| 5
| 128
| 1
| 10
| 10
|
|
|
|
|
|- align="center"
| 5
| 128
| 1
| 5
| 10
|
|
|
|
|
|- align="center"
| 5
| 256
| 1
| 10
| 100
|
|
|
|
|
|- align="center"
| 5
| 256
| 1
| 5
| 100
|
|
|
|
|
|- align="center"
| 5
| 256
| 1
| 10
| 10
|
|
|
|
|
|- align="center"
| 5
| 256
| 1
| 5
| 10
|
|
|
|
|
|}


==== Spheric Gaussian Fields Collection ====
== [http://www.ehu.eus/ccwintco/uploads/3/36/MPC-double-pendulum.zip Model Predictive control for a quad rotor moving a double pendulum] ==
{| border="1" cellpadding="10"
! colspan="3"|Parameters
! colspan="5"|Signal-to-Noise Ratio (SNR)
|-
! ne
! size
! theta1
! Without noise
! 20db
! 40db
! 60db
! 80db
|- align="center"
| 2
| 64
| 100
|
|
|
|
|
|- align="center"
| 2
| 64
| 50
|
|
|
|
|
|- align="center"
| 2
| 64
| 10
|
|
|
|
|
|- align="center"
| 2
| 128
| 100
|
|
|
|
|
|- align="center"
| 2
| 128
| 50
|
|
|
|
|
|- align="center"
| 2
| 128
| 10
|
|
|
|
|
|- align="center"
| 2
| 256
| 100
|
|
|
|
|
|- align="center"
| 2
| 256
| 50
|
|
|
|
|
|- align="center"
| 2
| 256
| 10
|
|
|
|
|
|- align="center"
| 3
| 64
| 100
|
|
|
|
|
|- align="center"
| 3
| 64
| 50
|
|
|
|
|
|- align="center"
| 3
| 64
| 10
|
|
|
|
|
|- align="center"
| 3
| 128
| 100
|
|
|
|
|
|- align="center"
| 3
| 128
| 50
|
|
|
|
|
|- align="center"
| 3
| 128
| 10
|
|
|
|
|
|- align="center"
| 3
| 256
| 100
|
|
|
|
|
|- align="center"
| 3
| 256
| 50
|
|
|
|
|
|- align="center"
| 3
| 256
| 10
|
|
|
|
|
|- align="center"
| 4
| 64
| 100
|
|
|
|
|
|- align="center"
| 4
| 64
| 50
|
|
|
|
|
|- align="center"
| 4
| 64
| 10
|
|
|
|
|
|- align="center"
| 4
| 128
| 100
|
|
|
|
|
|- align="center"
| 4
| 128
| 50
|
|
|
|
|
|- align="center"
| 4
| 128
| 10
|
|
|
|
|
|- align="center"
| 4
| 256
| 100
|
|
|
|
|
|- align="center"
| 4
| 256
| 50
|
|
|
|
|
|- align="center"
| 4
| 256
| 10
|
|
|
|
|
|- align="center"
| 5
| 64
| 100
|
|
|
|
|
|- align="center"
| 5
| 64
| 50
|
|
|
|
|
|- align="center"
| 5
| 64
| 10
|
|
|
|
|
|- align="center"
| 5
| 128
| 100
|
|
|
|
|
|- align="center"
| 5
| 128
| 50
|
|
|
|
|
|- align="center"
| 5
| 128
| 10
|
|
|
|
|
|- align="center"
| 5
| 256
| 100
|
|
|
|
|
|- align="center"
| 5
| 256
| 50
|
|
|
|
|
|- align="center"
| 5
| 256
| 10
|
|
|
|
|
|}


==== Exponential Gausian Fields Collection ====
== agentes inteligentes en videojuegos producido por Tai Nuñez [https://doi.org/10.5281/zenodo.3250975] ==
{| border="1" cellpadding="10"
! colspan="4"|Parameters
! colspan="5"|Signal-to-Noise Ratio (SNR)
|-
! ne
! size
! theta1
! theta2
! Without noise
! 20db
! 40db
! 60db
! 80db
|- align="center"
| 2
| 64
| 0.7
| 1.3
| [[Media:2e64x64ExponentialGFDefault.zip | download]]
| [[Media:2e64x64ExponentialGFDefaultSNR20.zip | download]]
| [[Media:2e64x64ExponentialGFDefaultSNR40.zip | download]]
| [[Media:2e64x64ExponentialGFDefaultSNR60.zip | download]]
| [[Media:2e64x64ExponentialGFDefaultSNR80.zip | download]]
|- align="center"
| 2
| 64
| 0.5
| 0.8
| [[Media:2e64x64ExponentialGFTheta1_0_5Theta2_0_8.zip | download]]
| [[Media:2e64x64ExponentialGFTheta1_0_5Theta2_0_8SNR20.zip | download]]
| [[Media:2e64x64ExponentialGFTheta1_0_5Theta2_0_8SNR40.zip | download]]
| [[Media:2e64x64ExponentialGFTheta1_0_5Theta2_0_8SNR60.zip | download]]
| [[Media:2e64x64ExponentialGFTheta1_0_5Theta2_0_8SNR80.zip | download]]
|- align="center"
| 2
| 64
| 0.9
| 1.9
| [[Media:2e64x64ExponentialGFTheta1_0_9Theta2_1_9.zip | download]]
| [[Media:2e64x64ExponentialGFTheta1_0_9Theta2_1_9SNR20.zip | download]]
| [[Media:2e64x64ExponentialGFTheta1_0_9Theta2_1_9SNR40.zip | download]]
| [[Media:2e64x64ExponentialGFTheta1_0_9Theta2_1_9SNR60.zip | download]]
| [[Media:2e64x64ExponentialGFTheta1_0_9Theta2_1_9SNR80.zip | download]]
|- align="center"
| 2
| 128
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 2
| 128
| 0.5
| 0.8
|
|
|
|
|
|- align="center"
| 2
| 128
| 0.9
| 1.9
|
|
|
|
|
|- align="center"
| 2
| 256
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 2
| 256
| 0.5
| 0.8
|
|
|
|
|
|- align="center"
| 2
| 256
| 0.9
| 1.9
|
|
|
|
|
|- align="center"
| 3
| 64
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 3
| 64
| 0.5
| 0.8
|
|
|
|
|
|- align="center"
| 3
| 64
| 0.9
| 1.9
|
|
|
|
|
|- align="center"
| 3
| 128
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 3
| 128
| 0.5
| 0.8
|
|
|
|
|
|- align="center"
| 3
| 128
| 0.9
| 1.9
|
|
|
|
|
|- align="center"
| 3
| 256
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 3
| 256
| 0.5
| 0.8
|
|
|
|
|
|- align="center"
| 3
| 256
| 0.9
| 1.9
|
|
|
|
|
|- align="center"
| 4
| 64
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 4
| 64
| 0.5
| 0.8
|
|
|
|
|
|- align="center"
| 4
| 64
| 0.9
| 1.9
|
|
|
|
|
|- align="center"
| 4
| 128
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 4
| 128
| 0.5
| 0.8
|
|
|
|
|
|- align="center"
| 4
| 128
| 0.9
| 1.9
|
|
|
|
|
|- align="center"
| 4
| 256
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 4
| 256
| 0.5
| 0.8
|
|
|
|
|
|- align="center"
| 4
| 256
| 0.9
| 1.9
|
|
|
|
|
|- align="center"
| 5
| 64
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 5
| 64
| 0.5
| 0.8
|
|
|
|
|
|- align="center"
| 5
| 64
| 0.9
| 1.9
|
|
|
|
|
|- align="center"
| 5
| 128
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 5
| 128
| 0.5
| 0.8
|
|
|
|
|
|- align="center"
| 5
| 128
| 0.9
| 1.9
|
|
|
|
|
|- align="center"
| 5
| 256
| 0.7
| 1.3
|
|
|
|
|
|- align="center"
| 5
| 256
| 0.5
| 0.8
|
|
|
|
|
|- align="center"
| 5
| 256
| 0.9
| 1.9
|
|
|
|
|
|}


==== Rational Gausian Fields Collection ====
==[https://github.com/borjafdezgauna/SimionZoo/wiki A framework for Reinforcement Learning algorithms development in C and Windows] ==
{| border="1" cellpadding="10"
! colspan="4"|Parameters
! colspan="5"|Signal-to-Noise Ratio (SNR)
|-
! ne
! size
! theta1
! theta2
! Without noise
! 20db
! 40db
! 60db
! 80db
|- align="center"
| 2
| 64
| 1.6
| 1.5
| [[Media:2e64x64RationalGFDefault.zip | download]]
| [[Media:2e64x64RationalGFDefaultSNR20.zip | download]]
| [[Media:2e64x64RationalGFDefaultSNR40.zip | download]]
| [[Media:2e64x64RationalGFDefaultSNR60.zip | download]]
| [[Media:2e64x64RationalGFDefaultSNR80.zip | download]]
|- align="center"
| 2
| 64
| 1.2
| 0.85
| [[Media:2e64x64RationalGFTheta1_1_2Theta2_0_85.zip | download]]
| [[Media:2e64x64RationalGFTheta1_1_2Theta2_0_85SNR20.zip | download]]
| [[Media:2e64x64RationalGFTheta1_1_2Theta2_0_85SNR40.zip | download]]
| [[Media:2e64x64RationalGFTheta1_1_2Theta2_0_85SNR60.zip | download]]
| [[Media:2e64x64RationalGFTheta1_1_2Theta2_0_85SNR80.zip | download]]
|- align="center"
| 2
| 64
| 0.85
| 2
| [[Media:2e64x64RationalGFTheta1_0_85Theta2_2.zip | download]]
| [[Media:2e64x64RationalGFTheta1_0_85Theta2_2SNR20.zip | download]]
| [[Media:2e64x64RationalGFTheta1_0_85Theta2_2SNR40.zip | download]]
| [[Media:2e64x64RationalGFTheta1_0_85Theta2_2SNR60.zip | download]]
| [[Media:2e64x64RationalGFTheta1_0_85Theta2_2SNR80.zip | download]]
|- align="center"
| 2
| 128
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 2
| 128
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 2
| 128
| 0.85
| 2
|
|
|
|
|
|- align="center"
| 2
| 256
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 2
| 256
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 2
| 256
| 0.85
| 2
|
|
|
|
|
|- align="center"
| 3
| 64
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 3
| 64
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 3
| 64
| 0.85
| 2
|
|
|
|
|
|- align="center"
| 3
| 128
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 3
| 128
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 3
| 128
| 0.85
| 2
|
|
|
|
|
|- align="center"
| 3
| 256
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 3
| 256
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 3
| 256
| 0.85
| 2
|
|
|
|
|
|- align="center"
| 4
| 64
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 4
| 64
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 4
| 64
| 0.85
| 2
|
|
|
|
|
|- align="center"
| 4
| 128
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 4
| 128
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 4
| 128
| 0.85
| 2
|
|
|
|
|
|- align="center"
| 4
| 256
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 4
| 256
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 4
| 256
| 0.85
| 2
|
|
|
|
|
|- align="center"
| 5
| 64
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 5
| 64
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 5
| 64
| 0.85
| 2
|
|
|
|
|
|- align="center"
| 5
| 128
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 5
| 128
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 5
| 128
| 0.85
| 2
|
|
|
|
|
|- align="center"
| 5
| 256
| 1.6
| 1.5
|
|
|
|
|
|- align="center"
| 5
| 256
| 1.2
| 0.85
|
|
|
|
|
|- align="center"
| 5
| 256
| 0.85
| 2
|
|
|
|
|
|}


==== Matern Gausian Fields Collection ====
==[[Software for vascular CTA segmentation]]==
{| border="1" cellpadding="10"
 
! colspan="4"|Parameters
==[[Robotic Vision: Technologies for Machine Learning and Vision Applications ]] ==
! colspan="5"|Signal-to-Noise Ratio (SNR)
 
|-
==[[Relevance Dendritic Computing: codes and examples]]==
! ne
 
! size
== [[Hybrid Image Segmentation]] ==
! theta1
 
! theta2
== [[Illumination Source Chromaticity Estimation (ISC)]] ==
! Without noise
 
! 20db
== [[A simulation environment in Matlab for Linked Muticomponent Robotic Systems (LMRS)]] ==
! 40db
 
! 60db
== [[media:MCNN_code.zip|Morphological Cellular Neural Network]] ==
! 80db
 
|- align="center"
== [[Coloreado de grafos con enjambres -- Swarm based graph coloring]] ==
| 2
 
| 64
== [[Hyperspectral Imagery Synthesis tools for MATLAB | Hyperspectral Image Synthesis toolbox for MATLAB]] ==
| 10
 
| 1
== [[Endmember Induction Algorithms (EIAs) for MATLAB and SCILAB | Endmember Induction Algorithms]] ==
| [[Media:2e64x64MaternGFDefault.zip | download]]
 
| [[Media:2e64x64MaternGFDefaultSNR20.zip | download]]
== [[Euclidean skeletons of digital image and volume data in linear time by integer medial axis transform]] ==
| [[Media:2e64x64MaternGFDefaultSNR40.zip | download]]
 
| [[Media:2e64x64MaternGFDefaultSNR60.zip | download]]
== [[Skeletonization, skeleton pruning and simple skeleton graph construction example in Matlab]] ==
| [[Media:2e64x64MaternGFDefaultSNR80.zip | download]]
 
|- align="center"
== [[NPP estimation software for regional/global applications using Forest-BGC]] ==
| 2
 
| 64
==[http://www.sc.ehu.es/ccwgrrom/webcaras/ Sistema de reconocimiento de caras desarrollado por Iñigo Barandiarán]==
| 5
 
| 0.5
== [[Graph Coloring Suite, Graph Coloring Solver and Graph generator]] ==
| [[Media:2e64x64MaternGFTheta1_5Theta2_0_5.zip | download]]
 
| [[Media:2e64x64MaternGFTheta1_5Theta2_0_5SNR20.zip | download]]
==[https://github.com/alexsavio/aizkolari#aizkolari Aizkolari: Supervised feature extraction from brain MRI (or any kind Nifti or Analyze files) and supervised classification tool for disease detection] [[Media:Alexsavio-aizkolari.zip | Local copy]]==
| [[Media:2e64x64MaternGFTheta1_5Theta2_0_5SNR40.zip | download]]
 
| [[Media:2e64x64MaternGFTheta1_5Theta2_0_5SNR60.zip | download]]
== [http://www.ehu.es/ccwintco/index.php/Usuario:Alexsavio#Software Other tools from Alexandre Savio] ==
| [[Media:2e64x64MaternGFTheta1_5Theta2_0_5SNR80.zip | download]]
 
|- align="center"
==[[Hybrid Extreme Rotation Forest (HERF)]]==
| 2
 
| 64
== [http://www.ehu.es/ccwintco/uploads/6/69/HERF.tar.gz Hybrid Extreme Rotation Forest (HERF) basic code] ==
| 20
 
| 2
== [http://www.ehu.es/ccwintco/uploads/2/26/AdaHERF.zip Adaptative Hybrid Extreme Rotation Forest (AdaHERF) python code] ==
| [[Media:2e64x64MaternGFTheta1_20Theta2_2.zip | download]]
 
| [[Media:2e64x64MaternGFTheta1_20Theta2_2SNR20.zip | download]]
== [[Hyperspectral Image Nonlinear Unmixing and Reconstruction by ELM Regression Ensemble (MATLAB) | Nonlinear Unmixing and Reconstruction]] ==
| [[Media:2e64x64MaternGFTheta1_20Theta2_2SNR40.zip | download]]
 
| [[Media:2e64x64MaternGFTheta1_20Theta2_2SNR60.zip | download]]
== [http://www.ehu.es/ccwintco/index.php?title=D-RR-QL Distributed Round-Robin Q-Learning (D-RR-QL) for Linked Multicomponent Robotic Systems] ==
| [[Media:2e64x64MaternGFTheta1_20Theta2_2SNR80.zip | download]]
 
|- align="center"
== [http://www.ehu.eus/ccwintco/index.php?title=Neurocomputing-RL-experiments Experiments of Conditioned Reinforcement Learning in Continuous Space Control Tasks] ==
| 2
 
| 128
== [http://www.ehu.eus/ccwintco/index.php?title=SimionZoo SimionZoo] ==
| 10
 
| 1
== [http://www.ehu.es/ccwintco/uploads/c/c8/DRIVE_Active_Learning_RF.zip Drive dataset with Active Learning and Random Forests] ==
|
 
|
== [[Influence Maximization]] ==
|
|
|
|- align="center"
| 2
| 128
| 5
| 0.5
|
|
|
|
|
|- align="center"
| 2
| 128
| 20
| 2
|
|
|
|
|
|- align="center"
| 2
| 256
| 10
| 1
|
|
|
|
|
|- align="center"
| 2
| 256
| 5
| 0.5
|
|
|
|
|
|- align="center"
| 2
| 256
| 20
| 2
|
|
|
|
|
|- align="center"
| 3
| 64
| 10
| 1
|
|
|
|
|
|- align="center"
| 3
| 64
| 5
| 0.5
|
|
|
|
|
|- align="center"
| 3
| 64
| 20
| 2
|
|
|
|
|
|- align="center"
| 3
| 128
| 10
| 1
|
|
|
|
|
|- align="center"
| 3
| 128
| 5
| 0.5
|
|
|
|
|
|- align="center"
| 3
| 128
| 20
| 2
|
|
|
|
|
|- align="center"
| 3
| 256
| 10
| 1
|
|
|
|
|
|- align="center"
| 3
| 256
| 5
| 0.5
|
|
|
|
|
|- align="center"
| 3
| 256
| 20
| 2
|
|
|
|
|
|- align="center"
| 4
| 64
| 10
| 1
|
|
|
|
|
|- align="center"
| 4
| 64
| 5
| 0.5
|
|
|
|
|
|- align="center"
| 4
| 64
| 20
| 2
|
|
|
|
|
|- align="center"
| 4
| 128
| 10
| 1
|
|
|
|
|
|- align="center"
| 4
| 128
| 5
| 0.5
|
|
|
|
|
|- align="center"
| 4
| 128
| 20
| 2
|
|
|
|
|
|- align="center"
| 4
| 256
| 10
| 1
|
|
|
|
|
|- align="center"
| 4
| 256
| 5
| 0.5
|
|
|
|
|
|- align="center"
| 4
| 256
| 20
| 2
|
|
|
|
|
|- align="center"
| 5
| 64
| 10
| 1
|
|
|
|
|
|- align="center"
| 5
| 64
| 5
| 0.5
|
|
|
|
|
|- align="center"
| 5
| 64
| 20
| 2
|
|
|
|
|
|- align="center"
| 5
| 128
| 10
| 1
|
|
|
|
|
|- align="center"
| 5
| 128
| 5
| 0.5
|
|
|
|
|
|- align="center"
| 5
| 128
| 20
| 2
|
|
|
|
|
|- align="center"
| 5
| 256
| 10
| 1
|
|
|
|
|
|- align="center"
| 5
| 256
| 5
| 0.5
|
|
|
|
|
|- align="center"
| 5
| 256
| 20
| 2
|
|
|
|
|
|}

Revisión actual - 19:43 1 jun 2021


Model Predictive control for a quad rotor moving a double pendulum

agentes inteligentes en videojuegos producido por Tai Nuñez [1]

A framework for Reinforcement Learning algorithms development in C and Windows

Software for vascular CTA segmentation

Robotic Vision: Technologies for Machine Learning and Vision Applications

Relevance Dendritic Computing: codes and examples

Hybrid Image Segmentation

Illumination Source Chromaticity Estimation (ISC)

A simulation environment in Matlab for Linked Muticomponent Robotic Systems (LMRS)

Morphological Cellular Neural Network

Coloreado de grafos con enjambres -- Swarm based graph coloring

Hyperspectral Image Synthesis toolbox for MATLAB

Endmember Induction Algorithms

Euclidean skeletons of digital image and volume data in linear time by integer medial axis transform

Skeletonization, skeleton pruning and simple skeleton graph construction example in Matlab

NPP estimation software for regional/global applications using Forest-BGC

Sistema de reconocimiento de caras desarrollado por Iñigo Barandiarán

Graph Coloring Suite, Graph Coloring Solver and Graph generator

Aizkolari: Supervised feature extraction from brain MRI (or any kind Nifti or Analyze files) and supervised classification tool for disease detection Local copy

Other tools from Alexandre Savio

Hybrid Extreme Rotation Forest (HERF)

Hybrid Extreme Rotation Forest (HERF) basic code

Adaptative Hybrid Extreme Rotation Forest (AdaHERF) python code

Nonlinear Unmixing and Reconstruction

Distributed Round-Robin Q-Learning (D-RR-QL) for Linked Multicomponent Robotic Systems

Experiments of Conditioned Reinforcement Learning in Continuous Space Control Tasks

SimionZoo

Drive dataset with Active Learning and Random Forests

Influence Maximization

Obtenido de «http://ehu.eus/ccwintco/index.php?title=GIC-source-code-free-libre&oldid=19979»