HiperLearn: A High Performance Learning Architecture
2002 (English)Report (Other academic)
A new architecture for learning systems has been developed. A number of particular design features in combination result in a very high performance and excellent robustness. The architecture uses a monopolar channel information representation. The channel representation implies a partially overlapping mapping of signals into a higher-dimensional space, such that a flexible but continuous restructuring mapping can be made. The high-dimensional mapping introduces locality in the information representation, which is directly available in wavelets or filter outputs. Single level maps using this representation can produce closed decision regions, thereby eliminating the need for costly back-propagation. The monopolar property implies that data only utilizes one polarity, say positive values, in addition to zero, allowing zero to represent no information. This leads to an efficient sparse representation.
The processing mode of the architecture is association where the mapping of feature inputs onto desired state outputs is learned from a representative training set. The sparse monopolar representation together with locality, using individual learning rates, allows a fast optimization, as the system exhibits linear complexity. Mapping into multiple channels gives a strategy to use confidence statements in data, leading to a low sensitivity to noise in features. The result is an architecture allowing systems with a complexity of some hundred thousand features described by some hundred thousand samples to be trained in typically less than an hour. Experiments that demonstrate functionality and noise immunity are presented. The architecture has been applied to the design of hyper complex operations for view centered object recognition in robot vision.
Place, publisher, year, edition, pages
2002. , 20 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2409
channel representation, monopolar, association
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-36330ISRN: LiTH-ISY-R-2409Local ID: 30988OAI: oai:DiVA.org:liu-36330DiVA: diva2:257178