FROM WISARD TO MAGNUS: A FAMILY OF WEIGHTLESS VIRTUAL NEURAL MACHINES
1998; World Scientific; Linguagem: Inglês
10.1142/9789812816849_0002
ISSN2010-2895
Autores Tópico(s)Neural Networks and Applications
ResumoProgress in Neural ProcessingRam-Based Neural Networks, pp. 18-30 (1998) No AccessFROM WISARD TO MAGNUS: A FAMILY OF WEIGHTLESS VIRTUAL NEURAL MACHINESIGOR ALEKSANDERIGOR ALEKSANDERDepartment of Electrical and Electronic Engineering, Imperial College of Science Technology and Medicine, London, UKhttps://doi.org/10.1142/9789812816849_0002Cited by:15 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: This chapter reviews a progression of weightless systems from the WISARD single-layer pattern recogniser to recent work on MAGNUS, a state machine designed to store sensory experience in its state structure. The stress is on algorithmic effects, that is, the effect of mapping these systems into conventional processors as virtual neural machines. The chapter briefly reviews the changes from the generalisation of discriminators in WISARD to generalising RAM (G-RAM) as currently used in MAGNUS systems. This leads to the introduction to MACCON (the Machine Consciousness Toolbox) a flexible version of MAGNUS which runs on current PC operating systems. FiguresReferencesRelatedDetailsCited By 15Cited by lists all citing articles based on Crossref citation.Memory efficient sign language recognition system based on WiSARD weightless neural network techniqueFaisal Ghazanfar and Najmi Ghani Haider1 Nov 2016Compressing VG-RAM WNN memory for lightweight applicationsEdilson de Aguiar, Avelino Forechi, Lucas Veronese, Mariella Berger and Alberto F. De Souza et al.1 Jul 2014Programming a VG-RAM based Neural Network ComputerAlberto F. De Souza, Avelino Forechi, Filipe Wall Mutz, Mariella Berger and Thiago Oliveira-Santos et al.1 Jul 2014An Empirical Study of the Influence of Data Structures on the Performance of VG-RAM ClassifiersDaniel S.F. Alves, Douglas O. Cardoso, Hugo C.C. Carneiro, Felipe M.G. Franca and Priscila M.V. Lima1 Sep 2013Traffic sign detection with VG-RAM weightless neural networksAlberto F. De Souza, Cayo Fontana, Filipe Mutz, Tiago Alves de Oliveira and Mariella Berger et al.1 Aug 2013Stereo matching with VG-RAM Weightless Neural NetworksLucas de Paula Veronese, Lauro Jose Lyrio Junior, Filipe Wall Mutz, Jorcy de Oliveira Neto and Vitor Barbirato Azevedo et al.1 Nov 2012Traffic sign recognition with VG-RAM Weightless Neural NetworksM. Berger, A. Forechi, A. F. D. Souza, J. de Oliveira Neto and L. Veronese et al.1 Nov 2012Fast learning and predicting of stock returns with virtual generalized random access memory weightless neural networksAlberto F. De Souza, Fabio Daros Freitas and André Gustavo Coelho de Almeida14 June 2011 | Concurrency and Computation: Practice and Experience, Vol. 24, No. 8Learning and Generalization in Random Automata NetworksAlireza Goudarzi, Christof Teuscher and Natali Gulbahce1 Jan 2012Towards Cognitive MachinesWitold Kinsner1 Jan 2011Automated multi-label text categorization with VG-RAM weightless neural networksAlberto F. De Souza, Felipe Pedroni, Elias Oliveira, Patrick M. Ciarelli and Wallace Favoreto Henrique et al.1 Jun 2009 | Neurocomputing, Vol. 72, No. 10-12Face Recognition with VG-RAM Weightless Neural NetworksAlberto F. De Souza, Claudine Badue, Felipe Pedroni, Elias Oliveira and Stiven Schwanz Dias et al.1 Jan 2008Automated Free Text Classification of Economic Activities Using VG-RAM Weightless Neural NetworksAlberto F. de Souza, Felipe Pedroni, Elias Oliveira, Patrick M. Ciarelli and Wallace F. Henrique et al.1 Oct 2007Improving VG-RAM Neural Networks Performance Using Knowledge CorrelationRaphael V. Carneiro, Stiven S. Dias, Dijalma Fardin, Hallysson Oliveira and Artur S. d’Avila Garcez et al.1 Jan 2006Challenges in the Design of Adoptive, Intelligent and Cognitive SystemsW. Kinsner Recommended Ram-Based Neural NetworksMetrics History PDF download
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