Associative Completion and Investment Learning using PSOMs

Walter JA, Ritter H. Associative Completion and Investment Learning using PSOMs. In: Malsburg von der C, Seelen von W, Vorbrüggen J, Sendhoff B, eds. Artificial Neural Networks — ICANN 96: 1996 International Conference Bochum, Germany, July 16–19, 1996 Proceedings. Lecture Notes in Computer Science. Vol 1112. Berlin: Springer; 1996: 157-164.We describe a hierarchical scheme for rapid adaptation of context dependent “skills”. The underlying idea is to first invest some learning effort to specialize the learning system to become a rapid learner for a restricted range of contexts. This is achieved by constructing a “Meta-mapping” that replaces an slow and iterative context adaptation by a “one-shot adaptation”, which is a context-dependent skill-reparameterization. The notion of “skill” is very general and includes a task specific, hand-crafted function mapping with context dependent parameterization, a complex control system, as well as a general learning system. A representation of a skill that is particularly convenient for the investment learning approach is by a Parameterized Self-Organizing Map (PSOM). Its direct constructability from even small data sets significantly simplifies the investment learning stage; its ability to operate as a continuous associative memory allows to represent skills in the form of “multi-way” mappings (relations) and provides an automatic mechanism for sensor data fusion. We demonstrate the concept in the context of a (synthetic) vision task that involves the associative completion of a set of feature locations and the task of one-shot adaptation of the transformation between world and object coordinates to a changed camera view of the object

Associative Completion and Investment Learning using PSOMs

We describe a hierarchical scheme for rapid adaptation of context dependent "skills". The underlying idea is to first invest some learning effort to specialize the learning system to become a rapid learner for a restricted range of contexts. This is achieved by constructing a "Meta-mapping" that replaces an slow and iterative context adaptation by a "one-shot adaptation", which is a context-dependent skill-reparameterization. The notion of "skill" is very general and includes a task specific, hand-crafted function mapping with context dependent parameterization, a complex control system, as well as a general learning system. A representation of a skill that is particularly convenient for the investment learning approach is by a Parameterized Self-Organizing Map (PSOM). Its direct constructability from even small data sets significantly simplifies the investment learning stage; its ability to operate as a continuous associative memory allows to represent skills in the form of "multi-way..