Learning Vector Quantization (LVQ)
Text Dependent Multilingual Speaker Identification using Learning Vector Quantization and PSO GA Hybrid Model
... system is proposed. The feature extraction techniques employed in system extract Mel frequency cepstral coefficient (MFCC), delta mel frequency cepstral coefficient (DMFCC) and format frequency. The feature selection is ...
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Classification of meningiomas using discriminant wavelet packets and learning vector quantization
... Recently some work has gone into the detection and classification of brain tumors. One such effort in the domain of meningiomas was carried out by Lessman et al. [1] who studied the problem of content-based visualization ...
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Learning Vector Quantization (LVQ) Neural Network Approach for Multilingual Speech Recognition
... machine learning, speech and pattern recognition feature extraction plays a key ...the Learning Vector Quantization (LVQ) neural network for further ...
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Image Contrast Enhancement using Learning Vector Quantization
... Human is gifted by god with five senses – sight, hearing, touch, smell and taste – which humans use to perceive their environment. Out of these five senses, sight is the most powerful. Image Contrast Enhancement with ...
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Cervical Cell Classification using Learning Vector Quantization (LVQ) Based on Shape and Statistical Features
... can be used as classification’s input [4]. Agustina use the ratio of the nucleus to the cytoplasm and produces an accuracy of 78% for abnormal cells image [5]. Meutia use the image intensity values from segmented ...
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A Motivation Behavior Classification based on Multi Objective Optimization using Learning Vector Quantization for Serious Games
... the learning process and of the game for educational ...employs Learning Vector Quantization (LVQ) for optimizing the motivation behavior input classification of the ...neuron vector of ...
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Independent Feature Elimination in High Dimensional Data : Empirical Study by applying Learning Vector Quantization method
... The dimensionality of the data gets increased as the databases in general are modified to suit to the changing requirements of users. As a result, databases will be suffered with increased redundancy, which in turn ...
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LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data
... This paper has presented a new over-sampling method using codebooks obtained by Learning Vector Quantization. In general, even when an existing SMOTE is applied to a biomedical dataset, it is still ...
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The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network
... fonts, syntax, weather and environment, spacing etc. Therefore, most of the previous prescripts could not apply for all the countries in the real world, all the environments, all types of the Kingdom of Saudi Arabia ...
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Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms
... Based on this, our goal in this document is to investigate effective methods that produce accurate quantizations with data samples. One of the most popular procedure is Lloyd’s algorithm (see Lloyd, 2003) sometimes ...
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Boundary optimization of Ground Glass Opacity in CT images of lung cancer
... In computer aided diagnosis, pre-processing, segmentation, feature extraction and classification are the steps involved. For segmentation the boundary must be defined to get regional information inside the boundary. ...
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A Review: Methods for Sensor Identification using Neural Network
... ABSTRACT: It is crucial to quantify location of sensor in the dam, for this purport numbers of ,the sensor are placed at sundry locations in the dam. Central water and Power Research Station (CWPRS) provided data of Dam ...
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Pattern recognition for bivariate process mean shifts using feature based artificial neural network
... Identically and independently distributed LEEWMA Last value of exponentially weighted moving average LVQ Learning vector quantization MAT Mean x autocomelation 1MGUSUM Multivariate cumul[r] ...
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Survey on Effective Kidney Stone Disease Diagnosis Using ANN
... A Learning Vector Quantization Network (LVQ) has a first competitive layer and a second linear ...LVQ learning in the competitive layer is based on a set of input/target ...no learning ...
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Data Mining with Computational Intelligence Lipo Wang , Xiuju Fu pdf
... Two-stage training [17][22][36][264] is often used for constructing RBF neural networks. At the first stage, the hidden layer is constructed by selecting the center and the width for each hidden neuron using various ...
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ECG BEAT CLASSIFICATION USING CROSS-WAVELET AND LVQ
... 139 | P a g e important, yet usually hidden, information from the raw data. Even more important is the mixing of suitable feature extractor and pattern classifier such that they can operate in coordination to make an ...
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Brain MRI Classification Using PNN and Segmentation Using K Means Clustering
... Support Vector Machine (SVM), Accuracy of only 65% was ...on learning vector quantization (LVQ) with image and data analysis and manipulation techniques is proposed to carry out an automated ...
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CLASSIFICATION OF DISEASED PLANT LEAVES USING NEURAL NETWORK ALGORITHMS
... (FFNN), learning vector quantization (LVQ) and radial basis function network (RBF) were tested for two different diseased leaf image classifications such as bean and bitter gourd ...
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Fuzzy Modeling using Vector Quantization with Supervised Learning
... that learning methods of fuzzy mod- eling using vector quantization (VQ) and steepest descend method (SDM) are superior in the number of rules to other methods using only ...of learning all ...
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Face Recognition Using Vector Quantization Histogram and Support Vector Machine Classifier
... the learning-rate. Appropriate learning-rate is essential toward the minimum of function, and it gradually diminishes for converging when function value approximates a ...
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