Using the Hermite regression formula to design a neural architecture with automatic learning of the “hidden” activation functions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1792, Issue , 2000, Pages 226-237

  Gaglio, S ^a,c   Pilato, G ^a   Sorbello, F ^a,c   Vassallo, G ^b  

^a UNIVERSITY OF PALERMO   (Italy)

^b Centro per la Ricerca Elettronica in Sicilia (CRES)   (Italy)

^c CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CHEMICAL ACTIVATION; OPTIMIZATION;

ACTIVATION FUNCTIONS; AUTOMATIC-LEARNING; FEED-FORWARD NEURAL ARCHITECTURES; GENERALIZATION CAPABILITY; NEURAL ARCHITECTURES; OPTIMIZATION ALGORITHMS; REGRESSION FORMULAS; TRADITIONAL ARCHITECTURE;

NETWORK ARCHITECTURE;

EID: 23044519397     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

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