%0 Conference Paper %1 szepesvari1994a %A Szepesvári, Cs. %A Lörincz, A. %B ICANN %C Sorrento, Italy %D 1994 %E Marinaro, M. %E Morasso, P.G. %I IEEE %K learning, manifold networks neural theory, %P 671--674 %T Self-organized Learning of 3 Dimensions %V 2 %X The geometric learning capabilities of a competitive neural network are studied. It is shown that the appropriate selection of a neural activity function enables the learning of the 3D geometry of a world, from two of the 2D projections of 3D extended objects

@inproceedings{szepesvari1994a, abstract = {The geometric learning capabilities of a competitive neural network are studied. It is shown that the appropriate selection of a neural activity function enables the learning of the 3D geometry of a world, from two of the 2D projections of 3D extended objects}, added-at = {2020-03-17T03:03:01.000+0100}, address = {Sorrento, Italy}, author = {Szepesv{\'a}ri, {Cs}. and L{\"o}rincz, A.}, biburl = {https://www.bibsonomy.org/bibtex/2cf5f07ba9b32f5c4471f57f4b7bd9730/csaba}, booktitle = {ICANN}, date-added = {2010-08-28 17:38:14 -0600}, date-modified = {2010-11-25 00:58:22 -0700}, editor = {Marinaro, M. and Morasso, P.G.}, interhash = {dfd9d31eb9b288d90f6253469b7cc2df}, intrahash = {cf5f07ba9b32f5c4471f57f4b7bd9730}, keywords = {learning, manifold networks neural theory,}, pages = {671--674}, pdf = {papers/icann94.ps.pdf}, publisher = {IEEE}, timestamp = {2020-03-17T03:03:01.000+0100}, title = {Self-organized Learning of 3 Dimensions}, volume = 2, year = 1994 }