Geodesic topographic product: An improvement to measure topology preservation of self-organizing neural networks

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3315, Issue , 2004, Pages 841-850

  Revuelta, Francisco Flórez ^a   Chamizo, Juan Manuel García ^a   Rodríguez, José García ^a   Sáez, Antonio Hernández ^a  

^a UNIVERSITY OF ALICANTE   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONS; GRAPH THEORY; HEURISTIC METHODS; INFORMATION ANALYSIS; LINEARIZATION; MATHEMATICAL MODELS; TOPOLOGY; VECTORS; ARTIFICIAL INTELLIGENCE; GEODESY; NEURAL NETWORKS;

GEODESIC DISTANCE; GEODESIC TOPOGRAPHIC PRODUCT; REFERENCE VECTORS; SELF-ORGANIZING NEURAL NETWORKS; COMPETITIVE LEARNING; DISTANCE MEASURE; GEODESIC DISTANCES; INPUT SPACE; SELF-ORGANIZING NEURAL NETWORK; TOPOLOGY PRESERVATION;

NEURAL NETWORKS; TOPOLOGY;

EID: 22944436136     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30498-2_84     Document Type: Conference Paper

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