Revisão Bibliográfica: Learning Vector Quantization 28/04/2003

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Discriminant Analysis by Gaussian Mixtures - Hastie, Tibshirani (1996)     (35 citations)
representation. A technique known as Learning Vector Quantization or LVQ has received a lot of attention

Convergence Properties of the K-Means Algorithms - Bottou, Bengio (1995)     (18 citations)
mixtures, Radial Basis Functions, Learning Vector Quantization and some Hidden Markov Models)The

Learning a Local Similarity Metric for Case-Based Reasoning - Ricci, Avesani (1995)     (10 citations)

similar approaches and in particular of Learning Vector Quantization [13] can be found in [19]Lowe [15]

Recognizing Teleoperated Manipulations - Pook, Ballard (1993)     (14 citations)
under teleoperation, using Kohonen 's Learning Vector Quantization[6] 7]Different operators perform

Adapting the Museum: A Non-Intrusive User Modeling.. - Marti, Rizzo, Petroni, .. (1999)     (3 citations)
of a self-organization algorithm (Learning Vector Quantization) Kohonen et al.1992)which

Developing Population Codes By Minimizing Description Length - Zemel, Hinton (1994)     (11 citations)
the bits)For example, in competitive learning (vector quantization)the code is the identity of the

Learning without Local Minima in Radial Basis Function.. - Bianchini, Frasconi, Gori (1995)     (9 citations)
the computation of RBF to Kohonen 's Learning Vector Quantization (LVQ) 8]This comparison suggests

A Framework for the Cooperation of Learning Algorithms - Bottou, Gallinari (1991)     (16 citations)
of hybrid algorithms combining MLP and Learning Vector Quantization (Bollivier, Gallinari &Thiria,

A Connectionist Control Component for the Theorem Prover SETHEO - Goller (1994)     (6 citations)
as backpropagation, delta-bar-delta, learning vector quantization and self-organizing nets, and a Bayes

Evolutionary Learning of Nearest Neighbor MLP - Qiangfu Zhao Tatsuo (1996)     (5 citations)
feature map (SOFM) and the learning vector quantization (LVQ) algorithms of Kohonen, a large

Learning and Design of Principal Curves - Kégl, Krzyzak, Linder, Zeger (2000)     (1 citation)
principal curves, feature extraction, learning, vector quantization, convergence rates. B. K'egl and A.

Implementation and Comparison of Growing Neural Gas, Growing.. - Hamker, Heinke (1997)     (3 citations)
(MLP) competes with classifiers such as Learning Vector Quantization, Gaussian quadratic classifiers and

Active Learning with Local Models - Hasenjäger, Ritter (1998)     (2 citations)
learning, local models, query based learning, vector quantization 1 Introduction In supervised

Feature Transformation with Generalized Learning Vector.. - Mu-King Tsay Keh-Hwa (1999)     (1 citation)
Feature Transformation with Generalized Learning Vector Quantization for Hand-Written Chinese Character

Statistical Data Compression by Optimal Segmentation - Theory.. - Steiner (1999)     (1 citation)
set in each adaptation step. We use Learning Vector Quantization (LVQ, see Pollard [22]or Martinetz

Initialization of Adaptive Parameters in Density Networks - Wlodzislaw, Rafal, Norbert (1997)     (2 citations)
may be improved using one of the learning vector quantization (LVQ) procedures [4]Basically all

Using Self-Organizing Maps and Learning Vector Quantization for.. - Kurimo (1997)     (2 citations)
No. 87 Using Self-Organizing Maps and Learning Vector Quantization for Mixture Density Hidden Markov

New Ways To Use LVQ-Codebooks Together With Hidden Markov Models - Torkkola (1994)     (3 citations)
way to employ codebooks trained by Learning Vector Quantization together with hidden Markov models. In

Prototype-Based Minimum Classification Error / Generalized.. - McDermott, Katagiri (1994)     (3 citations)
reported high classification rates for Learning Vector Quantization (LVQ) networks trained to classify

Multimodal Human-Computer Interaction - Vo, Waibel (1993)     (3 citations)
additional experiments exploring Learning Vector Quantization (LVQ-2) and Multi-State Time Delay

Learning an Asymmetric and Anisotropic Similarity Metric for.. - Ricci, Avesani (1995)     (2 citations)
are [26, 11]The comparison with the Learning Vector Quantization method [11] is discussed with more

Mapping context dependent acoustic information into.. - Mäntysalo, Torkkola.. (1994)     (2 citations)
Markov model VQ Vector quantization LVQ Learning vector quantization OLVQ Optimized rate learning vector

Discriminative Training for Speech Recognition - McDermott (1997)     (1 citation)
. 33 3.2 The Learning Vector Quantization Algorithms .

An Adaptive Two-Stage Approach to Classification of.. - Iivarinen, Rauhamaa, Visa (1997)     (1 citation)
classication method based on a SOM, a Learning Vector Quantization [9]and a long feature vector

Optical Chinese Character Recognition using Probabilistic.. - Richard Romero David (1997)     (1 citation)
of neural network training, LVQ (Learning Vector Quantization) and DSM (Decision Surface Mapping,

Gated Experts for Classification of Financial Time Series - Vengerov (1997)     (1 citation)
for GE's in which the gate uses LVQ (Learning Vector Quantization) clustering algorithm for soft

Using SOM and LVQ for HMM training - Mikko Kurimo (1997)     (1 citation)
for the mixture densities. If the Learning Vector quantization (LVQ) 2]is used in the training

Training Mixture Density HMMs with SOM and LVQ - Kurimo (1997)     (1 citation)
as a mixture of Gaussian densities. The Learning Vector Quantization (LVQ) is used to increase the

Selective Use Of The Speech Spectrum And A Vqgmm.. - Lin, Jan, Che, Yuk.. (1996)     (1 citation)
females#For each of the speakers, a Learning Vector-Quantization #LVQ #3#codebook is generated using

An HMM-Based Legal Amount Field OCR System for Checks - Kornai, Mohiuddin, Connell (1995)     (1 citation) against two standard techniques, Learning Vector Quantization [10] and Multi-Layer Perceptrons [12]

Unsupervised Segmentation of Surface Defects - Iivarinen, Rauhamaa, Visa (1996)     (1 citation)
classification method based on a SOM, a Learning Vector Quantization [9]and a long feature vector

Progressive Classification In The Compressed Domain For.. - Vittorio Castelli (1996)     (1 citation)
classifiers)k-Nearest Neighbor, Learning Vector Quantization, clustering-based schemes [16]and

Practicing Q-Learning - Bruske, Ahrns, Sommer (1996)     (1 citation)
RBF networks with additional on-line learning vector quantization (adaptive perceptualization) and

Using Lvq To Enhance Semi-Continuous Hidden Markov Models For.. - Kurimo (1993)     (1 citation)
ability of the SCHMMs by applying Learning Vector Quantization. The SCHMMs are used for the modeling

Optimal Unsupervised Learning - Watkin, Nadal (1993)     (1 citation)
on cost functions [9]and Kohonen's Learning Vector Quantization algorithm [2, 10]These examples are

Designing Parallel Computers for Self Organizing Maps - Nordström (1992)     (1 citation)
a fine tuning of the SOFM model called learning vector quantization (LVQ) model has been suggested [29,

Training Continuous Density Hidden Markov Models In.. - Kurimo, Torkkola (1992)     (1 citation)
on Self-Organizing Maps (SOMs) and Learning Vector Quantization (LVQ) 6]Our framework is to

Adaptive Kernel Classifiers for Short-Duration Oceanic .. - Ghosh, Chakravarthy.. (1991)     (1 citation)
group of neural-like schemes such as Learning Vector Quantization (LVQ) have also gained considerable

Intelligent Query And Browsing Information Retrieval.. - Jong-Min Park Department  
the user concept. Kohonen's "windowed" Learning Vector Quantization algorithm is shown to be related to

MULTISOFT Machine - Patanè, Russo   to be solved. Key words: Unsupervised Learning, Vector Quantization, Clustering, Parallel,

Optimal decision surfaces in LVQ1 classification of patterns - Verleysen, Thissen, Legat (1993)  
to multidimenfional stimuli spaces. 2. Learning Vector Quantization (LVQ1) The LVQ1 algorithm can be

Robust RBF Networks - Bors, Pitas  
The first stage rely on a robust learning vector quantization approach which estimates the hidden

Density-Based Multiscale Data Condensation - Mitra, Murthy, Pal (2002)  
may be further refined using the learning vector quantization algorithms [14]Competitive learning

Self-Organizing Dialogue Management - Jokinen, Hurtig, Hynn, Kanto..  
self-organizing maps, especially the Learning Vector Quantization method, as a classification tool. In

Fully Automatic Clustering System - Patanè, Russo  
Keywords Clustering, Unsupervised Learning, Vector Quantization, FACS, ELBG I. Introduction Cluster

Competitive Radial Basis functions training for.. - Cosi, Frasconi.. (2000)  
Competitive radial basis functions Learning vector quantization Phone classi"cation Radial basis

An Experimental Comparison between Consistency-based and.. - Ferri, Mollineda, Vidal  
1-NN rule with regard to the different Learning Vector Quantization schemes is presented. In particular,

Competitive Reinforcement Learning for Combinatorial Problems - Abramson, Wechsler (2001)  
the competitive learning rule found in Learning Vector Quantization (LVQ) serves as a promising function

Can Automatic Personal Categorization deal with User.. - Goren-Bar, Kuflik  
of Self-Organizing Maps (SOM) and Learning Vector Quantization (LVQ) to automatic document

Rule Extraction from Self-Organizing Networks - Hammer, Rechtien, Strickert..  
Germany Abstract. Generalized relevance learning vector quantization (GRLVQ) 4] constitutes a prototype

Rule Extraction from Self-Organizing Maps - Hammer, Rechtien, Strickert..  
1 Abstract Generalized Relevance Learning Vector Quantization (GRLVQ) constitutes a prototype based

Supervised Neural Gas for Learning Vector Quantization - Villmann, Hammer, Strickert  
Supervised Neural Gas for Learning Vector Quantization Thomas Villmann, Barbara Hammer, 2

Discriminative Prototype-Based Methods For Speech Recognition - Mcdermott  
are then described. These include: Learning Vector Quantization (LVQ)and different schemes for

Batch-RLVQ - Hammer, Villmann  
Abstract. Recently a variation of learning vector quantization has been proposed in [1]which allows

Estimating Relevant Input Dimensions for Self-organizing.. - Hammer, Villmann (2001)  
a new scheme for enlarging generalized learning vector quantization with weighting factors for the several

Stable On-Line Evolutionary Learning of NN-MLP - Qiangfu Zh Ao  
of prototypes. For example, in the learning vector quantization (LVQ) algorithms of Kohonen, a large

An Adaptive Codebook Design Using the Branching Competitive.. - Xiong, King, Moon (2001)  
2 Data Clustering By Competitive Learning Vector quantization is a problem of data clustering.

C4.5 Decision Forests - Tin Kam Ho  
one class (for instances, partitions by learning vector quantization [4] and nearest-neighbor matching

Supervised Learning for Automatic Classification of Documents .. - Goren-Bar, al.  
A closely related algorithm is the Learning Vector Quantization (LVQ)which uses supervised learning

A Comparision Of Different Multi- Interval Discretization.. - Trautzsch, Perner  
introduced: the first one is based on Learning Vector Quantization (LVQ) described by Kohonen [Koh95] and

Bayes Risk Weighted VQ and Learning VQ - Richard Wesel And (1994)  
by Oehler et al.and Optimized Learning Vector Quantization 1 (OLVQ1) proposed by Kohonen et al.

A Learning Algorithm For Markov Decision Processes With.. - Baras, Borkar  
learning, Markov decision processes, learning vector quantization, stochastic approximation, dynamic

Indirect Unsupervised Training of Backpropagation Nets - Seiffert, al. (1999)  
as well as Counterpropagation (CP) and Learning Vector Quantization (LVQ) 2]In contrast an unsupervised

-096 A Novel Hierarchical Data Mining Algorithm for.. - Kayvan Najarian Rizwan (2000)  
is inspired by an extension of the Learning Vector Quantization (LVQ) network called the

Experimental study on the precision requirements of RBF.. - Vollmer, Strey (1999)  
of the learning set) of Optimized Learning Vector Quantization (OLVQ) were performed. During

The Constraint Based Decomposition (CBD) training architecture - Sorin Draghici State  
linear machine decision trees, CN2, learning vector quantization (LVQ)backpropagation, nearest

Bagged Clustering - Leisch (1999)  
bagging, bootstrap samples, k-means, learning vector quantization I. Introduction Clustering is an old
Controlling On-Line Adaptation of a Prototype-Based.. - Vuori, Laaksonen, Oja (2000)  
is based on a modified version of the Learning Vector Quantization (LVQ) 1]Depending on the classes of

LVQ as a feature transformation for HMMs - Torkkola (1994)  
of the discriminative power of Learning Vector Quantization in combination with continuous density

On Global Self-Organizing Maps. - Wodzisaw Duch And (1996)  
processes should use LVQ (Learning Vector Quantization) instead of SOM.Nevertheless the

High Speed and High Accuracy Rough Classification for.. - Yuji Waizumi Student  

Characters Using Hierarchical Learning Vector Quantization #Yuji WAIZUMI Student Member,

Seismic Events Discrimination Using a New FLVQ Clustering Model - Payam Nassery Karim  
SUMMARY In thispi er, the LVQ (Learning Vector Quantization) model and its variants are regarded

Practical Design Methodology for Commercial Automatic Coin.. - Juan Manuel Moreno  
[Werbos, 1974] learning algorithm, the Learning Vector Quantization (LVQ) Kohonen, 1990] models, the

Unsupervised image segmentation with the self-organizing map.. - Iivarinen, Visa  
classication method based on a SOM, a learning vector quantization, and the co-occurrence matrix. The

CNeT: Competitive Neural Trees for Pattern Classification - Behnke, Karayiannis (1996)  
that employed by the (unlabeled data) learning vector quantization (LVQ)an unsupervised learning

A Note on Learning Vector Quantization - de Sa, al. (1993)  
CA: Morgan Kaufmann. A Note on Learning Vector Quantization Virginia R. de Sa Department of

Non-Hierarchical Clustering with Rival Penalized Competitive.. - Irwin King And  
extension of Kohonen's supervised learning vector quantization algorithm LVQ2 [9]It can also be

A Scalable Bit-Sequential SIMD Array for Nearest-Neighbor.. - Neschen  
as during the learning phase in LVQ (Learning Vector Quantization"3] or the "k-means" method [4]the

Search and global minimization in similarity-based methods. - Duch, Grudzinski (1999)  
(RBFs)Multilayer Perceptrons (MLPs)Learning Vector Quantization (LVQ)may be presented in this form.

An Integrated Framework For Generalized Nearest Prototype.. - Kuncheva, Bezdek  
radial basis functions (RBF) networks learning vector quantization (LVQ) type classi ers and nearest

On the Performance of the HONG Network for Pattern.. - Atukorale, Suganthan.. (2000)  
the labeled network, the supervised learning vector quantization (LVQ) algorithm [8] is applied. The

Bundling Heterogeneous Classifiers with Advisor Perceptrons - Lee, IV (1997)  
network, projection pursuit regression, learning vector quantization, logistic regression 2 1

A Gradient Descent Training Algorithm for VQ Classification - Ulug, Ahalt  
Discriminant Machine, Gradient Descent, Learning Vector Quantization. y This research was supported by the

Neural Network Classifiers for Optical Chinese Character.. - Richard Romero Robert (1995)  
using the new feature space, Kohonen's Learning Vector Quantization and Geva and Sitte's Decision Surface

Driver-Adaptive Warning - System Prepared Honeywell  
yaw)Using another adaptive technique (learning vector quantization) on the relative heading data, and!.pdf

Data Compression And Statistical Inference - Strasser  
or a stochastic gradient method called learning vector quantization (LVQ, sometimes called Lloyd's

Feature extractor giving distortion invariant hierarchical.. - Lampinen (1991)  
neural network classifiers, like learning vector quantization, LVQ, 10] and multilayer perceptron,

Fast associative mapping with look-up tables - Lampinen, Smolander (1995)  
by any supervised network, like the learning vector quantization LVQ [2] in classification problems or

Wood Defect Recognition With Self-Organizing Feature Selection - Lampinen, Smolander (1994)  
Radial Basis Function network, or the Learning Vector Quantization, LVQ, network. Together with their

Combined Compression and Classification with Learning Vector.. - Baras, Dey (1999)  

Compression and Classification with Learning Vector Quantization John S. Baras y Subhrakanti Dey

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