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GECCO
2009
Springer
15 years 2 months ago
The sensitivity of HyperNEAT to different geometric representations of a problem
HyperNEAT, a generative encoding for evolving artificial neural networks (ANNs), has the unique and powerful ability to exploit the geometry of a problem (e.g., symmetries) by enc...
Jeff Clune, Charles Ofria, Robert T. Pennock
ROBOCUP
1999
Springer
129views Robotics» more  ROBOCUP 1999»
15 years 1 months ago
The Ulm Sparrows 99
In RoboCup-98, sparrows team worked hard just to get both a simulation and a middle size robot team to work and to successfully participate in a major tournament. For this year, we...
Stefan Sablatnög, Stefan Enderle, Mark Dettin...
92
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DAGSTUHL
2006
14 years 10 months ago
Hierarchies Relating Topology and Geometry
Cognitive Vision has to represent, reason and learn about objects in its environment it has to manipulate and react to. There are deformable objects like humans which cannot be des...
Walter G. Kropatsch, Yll Haxhimusa, Pascal Lienhar...
ESANN
2006
14 years 10 months ago
Data topology visualization for the Self-Organizing Map
The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, is very useful for processing data of high dimensionality and complexity. Visualization met...
Kadim Tasdemir, Erzsébet Merényi
IPM
2008
100views more  IPM 2008»
14 years 9 months ago
Query-level loss functions for information retrieval
Many machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieval. However, since...
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng W...