The 'Neural' Phonetic Typewriter
Computer
Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Lazy Learning Algorithms for Problems with Many Binary Features and Classes
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
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Self Organizing Features Maps are used for a variety of tasks in visualization and clustering, acting to transform data from a highdimensional original feature space to a (usually) two-dimensional grid. SOFMs use a similarity metric in the input space, and this composes individual feature differences in a way that is not always desirable. This paper introduces the concept of a Pareto SOFM, which partitions features into groups, defines separate metrics in each partition, and retrieves a set of prototypes that trade off matches in different partitions. It is suitable for a wide range of exploratory tasks, including visualization and clustering....