Multi-Layer Self-Organizing Semantic Maps

Hyperbolic Self-Organizing Maps for Semantic Navigation

We introduce a new type of Self-Organizing Map (SOM) to navigate in the Semantic Space of large text collections. We propose a “hyperbolic SOM” (HSOM) based on a regular tesselation of the hyperbolic plane, which is a non-euclidean space characterized by constant negative gaussian curvature. The exponentially increasing size of a neighborhood around a point in hyperbolic space provides more fre...

Self-Organizing Chinese and Japanese Semantic Maps

using game theory techniques in self-organizing maps training

شبکه خود سازمانده پرکاربردترین شبکه عصبی برای انجام خوشه بندی و کوانتیزه نمودن برداری است. از زمان معرفی این شبکه تاکنون، از این روش در مسائل مختلف در حوزه های گوناگون استفاده و توسعه ها و بهبودهای متعددی برای آن ارائه شده است. شبکه خودسازمانده از تعدادی سلول برای تخمین تابع توزیع الگوهای ورودی در فضای چندبعدی استفاده می کند. احتمال وجود سلول مرده مشکلی اساسی در الگوریتم شبکه خودسازمانده به حسا...

Multi - Winner Self - Organizing Maps by Reiner

Title of Dissertation: One-Shot Multi-Winner Self-Organizing Maps Reiner Schulz, Doctor of Philosophy, July 22, 2004 Dissertation directed by: Dr. James Reggia Department of Computer Science There exist two different approaches to self-organizing maps (SOMs). One approach, rooted in theoretical neuroscience, uses SOMs as computational models of biological cortex. The other approach, taken in co...

Self-Organizing Maps for Multi-Objective Optimization

This work introduces novel recombination and mutation operators for multi-objective evolutionary algorithms using self-organizing maps in the context of Pareto optimization. The self-organizing map is actively learning from the evolution path in order to adapt the mutation step size. Standard selection operators can be used in conjunction with these operators.