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learning vector quantization technique

Learning vector quantization for proximity data

Learning vector quantization for proximity data

... Nyström technique which enables a low-rank matrix approximation of the proximity matrix resulting in a linear time ...linear technique together with a reliable test about its suitability ...

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LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data

LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data

... Over-sampling Technique) [7] is a powerful over-sampling method that has shown a great deal of success in class imbalanced ...RAMO technique [9]. They have shown in their experiments that the ...

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Classification of meningiomas using discriminant wavelet packets and learning vector quantization

Classification of meningiomas using discriminant wavelet packets and learning vector quantization

... detection technique is extracting inherent features in an ...classification technique would only be as good as the features extracted from the pattern being ...

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Fast Learning Algorithm for Fuzzy Inference Systems using Vector Quantization

Fast Learning Algorithm for Fuzzy Inference Systems using Vector Quantization

... for learning with high dimensional spaces [4]– ...a vector quantization technique to determine the initial assignment [9], [12], ...Specifically, learning methods using VQ and SDM are ...

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Improving learning vector quantization using data  reduction

Improving learning vector quantization using data reduction

... reduction technique to identify the essential key features instead of just reducing the numbers of ...reduction technique based on the k-median clustering ...support vector machines is less than in ...

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Learning Vector Quantization (LVQ) Neural Network Approach for Multilingual Speech Recognition

Learning Vector Quantization (LVQ) Neural Network Approach for Multilingual Speech Recognition

... extraction technique, Learning Vector Quantization (LVQ) neural network used for Multilingual Speech Recognition and Language ...PSO technique is used in the second phase for the both ...

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ECG BEAT CLASSIFICATION USING CROSS-WAVELET AND LVQ

ECG BEAT CLASSIFICATION USING CROSS-WAVELET AND LVQ

... Learning Vector Quantization (LVQ) has been introduced by ...employs Learning Vector Quantization (LVQ) algorithms for the purpose of classification of ECG ...of vector ...

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Image Contrast Enhancement using Learning Vector Quantization

Image Contrast Enhancement using Learning Vector Quantization

... Linear Vector Quantization (MRHLVQ) filter technique is proposed for image contrast enhancement and brightness ...Liner Vector Quantization (LVQ)and histogram matching techniques ...

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Spoken Word Recognition Using MFCC and Learning Vector Quantization

Spoken Word Recognition Using MFCC and Learning Vector Quantization

... Learning Vector Quantization (LVQ) is a supervised version of vector quantization that can be used when we have each input data with class ...This learning technique uses ...

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Fuzzy Modeling using Vector Quantization based on Input and Output Learning Data

Fuzzy Modeling using Vector Quantization based on Input and Output Learning Data

... a vector quantization technique to determine the initial assignment of learning parameters [5], ...Specifically, learning methods using VQ and SDM are superior in the number of ...

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Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms

Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms

... the quantization problem and some results on the almost sure convergence of the competitive learning vector quantization (CLVQ) ...resulting technique the distributed asynchronous ...

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Combining dissimilarity measures for prototype-based classification

Combining dissimilarity measures for prototype-based classification

... a technique for combining different dissimilarity measures into a Learning Vector Quantization classification scheme for heterogeneous, mixed ...

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Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches

Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches

... Machine Learning Research, 5:975–1005, August ...Support Vector Machines with Embedded Reject ...Support Vector Machines (SVM2002), Niagara Falls, pages ...

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Regularization in Relevance Learning Vector Quantization Using l one Norms

Regularization in Relevance Learning Vector Quantization Using l one Norms

... sity in relevance learning, i.e. sparsity in the relevance profile [8]. It is based on the Least Absolute Selection and Shrinkage Operator approach (LASSO, [14]) but realizing a gradient descent learning ...

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Introduction to optimal vector quantization and its applications for numerics

Introduction to optimal vector quantization and its applications for numerics

... of quantization (dual quantization, see [61]) has refined this point for view by switching from Voronoi diagram to a direct approach of Delaunay triangulation which provides grids better fitted to ...

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Vector Quantization of Harmonic Magnitudes in Speech Coding Applications—A Survey and New Technique

Vector Quantization of Harmonic Magnitudes in Speech Coding Applications—A Survey and New Technique

... harmonic vector is upsampled by a factor of eight and interpolated to the fixed dimension of the VQ equal to ...After quantization, a similar double-interpolation procedure is applied to the 44-dimensional ...

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Voice Recognition Robotic Car

Voice Recognition Robotic Car

... The most general form of voice recognition can be done through feature analysis which usually leads to "speaker-independent" voice recognition. This method processes the voice input using Linear Predictive Coding ...

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The International Journal of Innovative Research in Science & Engineering (IJIRSE)

The International Journal of Innovative Research in Science & Engineering (IJIRSE)

... Two standard 512×512 monochromatic images, Barbara and Peppers, are used for codebook constructions. The selected training images are divided into small non-overlapping blocks of size 4×4 pixels making the vector ...

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Does Non-linearity Matter in Retail Credit Risk Modeling?

Does Non-linearity Matter in Retail Credit Risk Modeling?

... The learning process of the LVQ network follows rules which combine un- supervised and supervised ...fundamental learning rule is Kohonen’s LVQ1. Since it includes supervised learning, the network’s ...

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A 
		survey on neural network models for data analysis

A survey on neural network models for data analysis

... Artificial Neural Network (ANN) is an information-processing archetype that draws inspiration from biological nervous systems, like the brain, in order to process information. The key unit of this information processing ...

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