Revisão Bibliográfica: Learning Vector Quantization 28/04/2003
Pacotes Computacionais e Algoritmos Relacionados
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Pacotes Computacionais e Algoritmos Relacionados
: 92 8.6 Dynamic Learning Vector Quantization (DLVQ) ftp.ira.uka.de/pub/neuron/SNNS/SNNSv3.0.Manual.ps.gz
Lvq Pak: The Learning Vector Quantization Program Package Teuvo Kohonen, Jussi www.ele.etsmtl.ca/academique/ele778/articles/lvq_tr96.ps
6.1. Learning Vector Quantization em Computação 6.1.1. Redes Neurais Neural Networks and Statistical Models - Sarle (1994) (23 citations) NN models, such as counterpropagation, learning vector quantization, and self-organizing maps, that have ftp.sas.com/pub/neural/neural1.ps A statistical neural network for high-dimensional.. - Verleysen, Voz.. (1995) (3 citations) algorithm is based on a #competitive learning# vector quantization of the data# and on the choice of www.dice.ucl.ac.be/~verleyse/papers/icnn95mv.pdf The Neural Network Objects - Marcel Kunze, Johannes Steffens (1996) (2 citations) the following models are offered: Learning Vector Quantization (LVQ)Kohonen Feature Map (KFM) tau.ep1.ruhr-uni-bochum.de/pub/marcel/nno.ps.gz Performance of Digital Neuro-computers - Cornu (1994) (2 citations) self-organizing feature maps [9] and learning vector quantization [4] are examples of native on-line mantraftp.epfl.ch/mantra/cornu.uneuro94.ps.gz Neural Network based Segmentation of Magnetic.. - Alirezaie, Jernigan.. (1997) (1 citation) study, we present the application of a Learning Vector Quantization (LVQ) Artificial Neural Network (ANN) monet.uwaterloo.ca/~javad/TNS-97.ps.gz Stock Market Pattern Recognition with Neural Networks - Vengerov (1997) (1 citation) experts via a clustering algorithm LVQ (Learning Vector Quantization) as an alternative to linear www.stanford.edu/~vengerov/NNmod.ps Iterative Improvement of a Nearest Neighbor Classifier - Yau, Manry (1991) (2 citations) a neural network, which they have named learning vector quantization (LVQ)They have also suggested a www-ee.uta.edu/eeweb/ip/papers/yau.pdf An Evidence-Theoretic Neural Network Classifier - Denoeux (1995) (1 citation) classification scheme as compared to Learning Vector Quantization (LVQ) 6] and Radial Basis Functions www.hds.utc.fr/~tdenoeux/congres/SMC95.ps.Z A VLSI System for Neural Bayesian and LVQ Classification - Thissen, Verleysen.. (1995) boundaries between classes, and LVQ (Learning Vector Quantization) 3] or 1-NearestNeighbor for its www.dice.ucl.ac.be/~verleyse/papers/iwann95pt.pdf A Review of Artificial Neural Network Applications in.. - Perry And Pignatiello (BPN)simulated annealing (SA)learning vector quantization (LVQ)probabilistic neural network fie.engrng.pitt.edu/iie2002/proceedings/ierc/papers/2272.pdf Ensemble Self-Generating Neural Networks for Chaotic Time.. - Inoue, Narihisa (2000) competitive learning methods such as learning vector quantization (LVQ) 13] and traditional AI methods nari36a.ice.ous.ac.jp/~inoue/papers/ipmu00.ps.gz T-CombNET - A Neural Network Dedicated to Hand Gesture.. - Lamar, Bhuiyan, Iwata (2000) The Stem network is composed of a Learning Vector Quantization (LVQ) based NN and the Branch Networks www.eletrica.ufpr.br/lamar/public/bmcv2000.ps.gz A Comparative Study of Conventional and Neural Network.. - Multispectral Data.. the use of clustering approaches :the Learning Vector Quantization 2 (LVQ2) Kohonen,1989]the Kohonen perso-iti.enst-bretagne.fr/~solaiman/Documents/Publis/Pdf/A-IGARS2.pdf Studies on the Experimental Construction of a Neural.. - Pesonen (1998) algorithms, backpropagation (BP) and learning vector quantization (LVQ)classified patient cases with a www.cs.uku.fi/~epesonen/research/phdthesis.ps.gz Neural Network Systems, Techniques and Applications in.. - Lampinen, Laaksonen, Oja (1997) . 27 4.4.2 Learning Vector Quantization .www.lce.hut.fi/publications/ps/b1_nnsystems.ps Competitive Neural Trees for Vector Quantization - Behnke, Karayiannis that employed by the (unlabeled data) learning vector quantization (LVQ)an unsupervised learning www.inf.fu-berlin.de/~behnke/papers/nf96.ps.gz Interactive and Incremental Learning via a Mixture of.. - Qiong Liu Stephen as SOM (Self-Organizing Map) with LVQ (Learning Vector Quantization) 5, 6]GNG (Growing Neural Gas) with www.ifp.uiuc.edu/~yingwu/papers/JCIS00.ps Recognizing Local Weather Patterns with Traditional and Neural .. - Driesse, al. Feature Map and its enhancement, Learning Vector Quantization. A detailed discussion of the www.cas.mcmaster.ca/cas/research/dcssreports/DCSSTR9602.pdf Median Radial Basis Functions Neural Network - Bors, Pitas learning stage and it is based on the learning vector quantization algorithm and its second order poseidon.csd.auth.gr/papers/PUBLISHED/JOURNAL/Bors96c/Bors96c.ps.Z Comparison of Statistical and Neural Classifiers and Their.. - Alpaydin, Gürgen (1996) (a special case of which is the Learning Vector Quantization) As neural classifiers, we include www.cmpe.boun.edu.tr/~ethem/files/papers/aca.ps.Z Neural Networks from Similarity Based Perspective - Duch, Adamczak (2000) or other localized functions, or the Learning Vector Quantization (LVQ) method based on optimization of www.phys.uni.torun.pl/publications/kmk/00nn-sbm.pdf Floating Gaussian Mapping: a New Model of Adaptive Systems - Wlodzislaw Duch Adaptive (1994) as Artificial Neural Networks (ANNs)Learning Vector Quantization (LVQ) and self-organizing mappings www.phys.uni.torun.pl/publications/kmk/94fgm.pdf Alternative Neural Network Approaches to Corporate Bond Rating - Ravipim Chaveesuk Chat radial basis function and learning vector quantization for the task of rating U.S. corporate joy.eng.auburn.edu/users/aesmith/postscript/value.pdf NeuNIDA: Reasoning on Neural Net Algorithms as Basis for.. - Encarnacão (1993) LMS Backpropagation Reinforcement learning Vector Quantization Self-organizing maps Competitive www.gris.uni-tuebingen.de/gris/proj/guis/Ps/neunida93.ps.gz Design of neural classifiers using variable-length.. - Merelo, Prieto.. (1995) and a vectorial fitness to optimize Learning Vector Quantization (lvq )neural networks. The procedure ftp.santafe.edu/pub/jmerelo/g-lvq-NPL.ps Comparison of Neural and Statistical Classifiers.. - Holmström.. (1996) neural network classifiers included Learning Vector Quantization (LVQ) and Radial Basis Function (RBF) www.cis.hut.fi/~jorma/papers/rnia13.ps Signal Clustering Using Self-Organizing Neural Networks with.. - Chin-Der Wann processing problems for years. The learning vector quantization algorithm (Makhoul et al.1985) and www.cwc.nus.edu.sg/cwcdocs/zfiles/signal_clustering.ps.gz Combining Neural and Statistical Classifiers Via Perceptron - Lee tree, projection pursuit regression, learning vector quantization. Introduction The recent decade has www.cs.fit.edu/~imlm/imlm96/papers/lee-s.ps Artificial neural networks and statistical approaches to . . . - Suurmond, Bergkvist (1996) 6 4. Learning Vector Quantization www.iiasa.ac.at/Publications/Documents/WP-96-131.ps Neural Network Based Cloud Classifier - Visa, Iivarinen, Valkealahti.. Maps (SOM)which are fine-tuned by the Learning Vector Quantization (LVQ)The classification is done in www.cis.hut.fi/~jucca/cloud/icann95.ps.Z Signal Clustering Using Self-Organizing Neural Networks with.. - Chin-Der Wann processing problems for years. The learning vector quantization algorithm (Makhoul et al.1985) and www.cwc.nus.edu.sg/cwcdocs/zfiles/signal_clustering.ps.gz Combining Neural and Statistical Classifiers Via Perceptron - Lee tree, projection pursuit regression, learning vector quantization. Introduction The recent decade has www.cs.fit.edu/~imlm/imlm96/papers/lee-s.ps Artificial neural networks and statistical approaches to . . . - Suurmond, Bergkvist (1996) 6 4. Learning Vector Quantization www.iiasa.ac.at/Publications/Documents/WP-96-131.ps Neural Network Based Cloud Classifier - Visa, Iivarinen, Valkealahti.. Maps (SOM)which are fine-tuned by the Learning Vector Quantization (LVQ)The classification is done in www.cis.hut.fi/~jucca/cloud/icann95.ps.Z Adaptive Signal Clustering Using Self-Organizing Neural Networks - Chin-Der Wann processing problems for years. The learning vector quantization algorithm (Makhoul et al.1985) and www.cwc.nus.edu.sg/~cwcpub/zfiles/adaptive_signal.ps.gz Pattern Recognition via Neural Networks - Ripley also not probabilistic 2 Kohonen's learning vector quantization (LVQ) methods [18, 19, 20] are www.stats.ox.ac.uk/pub/neural/papers/PRNN.ps.Z 6.1.2. Fuzzy Sets
....concepts they determine the negative or positive effect of one concept on the others, with a fuzzy degree of causation. The determination of the degree of casual relationship among concepts can be improved by the application of learning rules for choosing appropriate weights for the FCM [8]. In this way, an expert decodes his own knowledge on the behavioral model of the system and transforms this knowledge in a weighted graph. 4 2.2.1.Assigning numerical weights Knowledge on the behaviour of a system is rather subjective and in order to construct a model of the system it is .... ....among concepts. This methodology may lead to a distorted model of the system because human factor is not always reliable. In order to refine the model of the system, learning rules are used to adjust weights of FCM interconnections. The Differential Hebbian learning rule has been proposed [8] to be used in the training of a specific type of FCMs. The Differential Hebbian learning law adjusts the weights of the interconnection between concepts It grows a positive edge between two concepts if they both increase or both decrease and it grows a negative edge if values of concepts move in ....
A first approach to a Taxonomy of Fuzzy-Neural Systems - Magdalena (1995) Neural Fuzzy Systems - Fullér (1995) Selection of Distance Metrics and Feature Subsets for k-Nearest.. - Barker (1997) Neurofuzzy Traffic Signal Control - Bingham (1998) Modeling the operation of Margolus quantum cellular automaton.. - Parviainen (2002) Implementation and Algorithms of a Tree Shape Parallel Computer - Hämäläinen (1996) Neurofuzzy Construction Algorithms - Bossley The Implementation Of Fuzzy Systems, Neural Networks and.. - Blake, McGuire, al. Neuro-Fuzzy Systems: Review And Prospects - Nauck (1997)
Self-organizing map - Kohonen - 1990 Book Details from Amazon or Barnes & Noble Fast learning in networks of locally-tuned processing units - Moody, Darken - 1989 the approximate realization of continuous mappings by neural.. - Funahashi - 1989 An introduction to computing with neural nets - Lippmann - 1987 Fuzzy identification of systems and its applications to mode.. - Takagi, Sugeno - 1985 Fuzzy logic in control systems: fuzzy logic controller -- pa.. - Lee - 1990 Fuzzy logic in control systems: fuzzy logic controller -- pa.. - Lee - 1990 ANFIS: adaptive-network-based fuzzy inference system - Jang - 1993 Orthogonal least squares learning algorithm for radial basis.. - Chen, Cowan et al. - 1991 Fuzzy ARTMAP: a neural network architecture for incremental .. - Carpenter, Grossberg et al. - 1993 Neural Networks for Optimization and Signal Processing - Cichocki, Unbehauen - 1992 Sparse Distributed Memory - Kanerva - 1988 Computer Controlled Systems: Theory and Design - Astrom, Wittenmark - 1990 Fuzzy ART: fast stable learning and categorization of analog.. - Carpenter, Grossberg et al. - 1991 Learning and tuning fuzzy logic controllers through reinforc.. - Berenji, Khedkar - 1993 Berlin: Springer Verlag - Kohonen, Associative et al. - 1988 Neural-network-based fuzzy logic control and decision system - Lin, Lee - 1991 the training of radial basis function classifiers - Musavi, Ahmed et al. - 1992 Learning to control an inverted pendulum using neural networ.. - Anderson - 1989 Functional equivalence between radial basis function network.. - Jang - 1993 Self-learning fuzzy controllers based on temporal back propa.. - Jang - 1992 Neural networks for self-learning control systems - Nguyen, Widrow - 1990 Variants of self-organizing maps - Kangas, Kohonen et al. - 1990 On fuzzy modeling using fuzzy neural networks with the backp.. - Horikawa, Furahashi et al. - 1992 Fuzzy logic Controllers - Berenji - 1992 A gaussian potential function network with hierarchically se.. - Lee, Kil - 1991 Adaptive fuzzy systems for backing up a truck-and-trailer - Kong, Kosko - 1992 Fuzzy Kohonen clustering networks - Tsao, Bezdek et al. - 1994 Fuzzy Kohonen clustering networks - Bezdek, Tsao et al. - 1992 Neural networks, principal components and subspaces - Oja - 1989 Fuzzy basis functions, universal approximation, and orthogon.. - Wang, Mendel - 1992 Neural networks in designing fuzzy systems for real world ap.. - Halgamuge, Glesner - 1994 A reinforcement learning-based architecture for fuzzy logic .. - Berenji - 1992 A fuzzy neural network learning fuzzy control rules and memb.. - Nauck, Kruse - 1993 Fuzzy self-organizing map - Vuorimaa - 1994 the principles of fuzzy neural networks - Gupta, Rao - 1994 Rule-base structure identification in an adaptive-network-ba.. - Sun - 1994 Neural Networks and Fuzzy Systems: A Dynamical Approach to M.. - Kosko - 1992 Book Details from Barnes & Noble Neural networks designed on approximate reasoning architectu.. - Takagi, Suzuki et al. - 1992 Combining neural networks and fuzzy controllers - Nauck, Klawonn et al. - 1993 Back-propagation neural networks for nonlinear self-tuning a.. - Chen - 1990 Fuzzy neural networks - Lee, Lee - 1975 Parallel self-organizing feature maps for unsupervised patte.. - Huntsberger, Ajjimarangsee - 1990 Neural networks for nonlinear internal model control - Hunt, Sbarbaro - 1991 Correlation based model validity tests for nonlinear model - Billings, Voon - 1986 Reinforcement structure/parameter learning for neuralnetwork.. - Lin, Lee - 1994 Multilayer perceptron, fuzzy sets, and classification - Pal, Mitra - 1992 An alternative approach for generation of membership functio.. - Halgamuge, Poechmueller et al. - 1994 Learning control using fuzzified self-organizing radial basi.. - Nie, Linkens - 1993 Fuzzy competitive learning - Chung, Lee - 1994 Fuzzy neural networks with reference neurons as pattern clas.. - Pedrycz - 1992 Analysis of flexible structured fuzzy logic controllers - Yager, Filev et al. - 1990 DVQ: Dynamic vector quantization-- an incremental LVQ - Poirier, Ferrieux - 1991 A fuzzy neural network model and its hardware implementation - Kuo, Kao et al. - 1993 Stochastic competitive learning - Kosko - 1991 A neuro-fuzzy system for chemical agent detection - Vuorimaa, Jukarainen - 1994 Use of the fuzzy self-organizing map in pattern recognition - Vuorimaa - 1994 A model-based neuro-fuzzy controller - Vuorimaa - 1994 Gas recognition using fuzzy self-organizing map - Jukarainen, Vuorimaa - 1994 Takaisinkytketty neuraaliverkkosäätäj - Ruoppila - 1992 A human vigilance analysis system using neural networks and .. - Tervonen, Vuorimaa et al. - 1994 Fuzzy rule generation based on a neural network approach - Thaler - 1993 Modelling of non-linear systems using radial basis function .. - Suontausta, Ruoppila et al. - 1994 Neural Network Approach to Fault Diagnosis - Sorsa - 1993 A self-organizing KNN fuzzy controller and its neural networ.. - Kwon, Zervakis - 1994 Fuzzy self-organizing map using Sugeno's fuzzy rules - Ojala, Vuorimaa - 1994 Prentice-Hall International - Ganong, Medical - 1987 Use of the default rule in fuzzy self-organizing map - Vuorimaa - 1994 Self-generating fuzzy self-organizing map - Vuorimaa Livros na Área: 
Fuzzy Cognitive Map In Modeling Supervisory Control Systems - Styrios, Groumpos ....concepts they determine the negative or positive effect of one concept on the others, with a fuzzy degree of causation. The determination of the degree of casual relationship among concepts can be improved by the application of learning rules for choosing appropriate weights for the FCM [8]. In this way, an expert decodes his own knowledge on the behavioral model of the system and transforms this knowledge in a weighted graph. 4 2.2.1.Assigning numerical weights Knowledge on the behaviour of a system is rather subjective and in order to construct a model of the system it is .... ....among concepts. This methodology may lead to a distorted model of the system because human factor is not always reliable. In order to refine the model of the system, learning rules are used to adjust weights of FCM interconnections. The Differential Hebbian learning rule has been proposed [8] to be used in the training of a specific type of FCMs. The Differential Hebbian learning law adjusts the weights of the interconnection between concepts It grows a positive edge between two concepts if they both increase or both decrease and it grows a negative edge if values of concepts move in .... B. Kosko, Neural Networks and Fuzzy Systems. Englewood Cliffs, N.J.: Prentice-Hall, 1992.
Neural Fuzzy Preference Integration using Neural Preference Moore .. - Wermter (2000) ....order in [0, 1] TM and have to find a useful partiaJ order 1 in [0, 1] TM. A simple generaJization of the order from [0, 1] to [0, 1] TM is possible with the dominance order. This m dimensionaJ order has already been widely used, for instance for certain applications in fuzzy reasoning [Kosko, 1992]. Therefore, we will consider this dominance order as a first alternative and will explain why the common dominance order is not yet sufficient for our preferences. Definition 3 (Dominance Order)Let a (al, am) b (bl, bm) be two m dimensional preferences from [0, 1] TM. Then the .... Kosko, B. (1992). Neural Networks and Fuzzy Systems. Prentice-Hall, Engle- wood Cliffs, NJ.
....in the solution space which result in creation of the fuzzy sets. The ANN learns these clusters based on actual human behavior test data. A further advantage is that the solution space rather than being represented point by point as some expert systems clumps the space as described by Kosko [2,3]. This results in fewer rules and lower computer resources. The experiment to build and validate the model includes a compensatory task performed by several human subjects to develop a training and test set of data in this human behavior. In the compensatory tracking task the subject attempts to .... Directory: pub -> docs -> vonzuben pub -> Asilomar Conference on Circuits, Systems & Computers, Pacific Grove, ca, November 1978, pp. 55 58 pub -> Forthcoming meetings and conferences pub -> Harmonised compatibility and sharing conditions for video pmse in the 7 9 ghz frequency band, taking into account radar use docs -> A dragon in the tube vonzuben -> Revisão bibliográfica redes neurais recorrentes vonzuben -> Revisão Bibliográfica: Autômatos Celulares docs -> Updated March 2017 Registration Quick Reference Card for Employees/Associates Download 488.92 Kb. Share with your friends:
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