learning vector quantization algorithms
Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms
... local algorithms do not have to wait at preset points for messages to become ...on-line algorithms avoid tremendous and non scalable batch requests on all data ...
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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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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 ...
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Brain MRI Classification Using PNN and Segmentation Using K Means Clustering
... Support Vector Machine (SVM), Accuracy of only 65% was ...clustering algorithms for detection of brain tumor is carried out, it observed that K-means algorithm produce more accurate result compared to Fuzzy ...
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Survey on Effective Kidney Stone Disease Diagnosis Using ANN
... network algorithms which have different architecture and ...used Learning vector quantization (LVQ), two layers feed forward perceptron trained with back propagation training algorithm and ...
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Process Based Online Contents with Offensive Content Detection
... machine learning Naïve Bayes remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, ...
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Speaker Identification by using Vector Quantization
... possible. Vector Quantization [1] - [4] is an efficient data compression technique and has been used in various applications involving VQ-based encoding and VQ based ...recognition. Vector ...
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Vector Quantization of True-Color Images
... the sum the of Mathematically, dimensions the product code product: m c = A C {all vectors = j=1 For with if example, rate R/k k-dimensional The product the design is a code x2'-, scalar[r] ...
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A survey on neural network models for data analysis
... number of learning techniques, especially back- propagation. Here the output values are compared with the correct answer to compute the value of some predefined error-function. The error is then fed back through ...
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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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Multiple-Description Multistage Vector Quantization
... be identical (i.e., a repetition code). This is the case in the first stage of System B and System C. Also note that the rest of the stages in these two systems have rate 0 (codebook size of 1) for one of the ...
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Boundary optimization of Ground Glass Opacity in CT images of lung cancer
... The objective of the algorithm for the LVQ net is to ascertain the output unit having a matching pattern with the input vector. As the process ends, if both ‘x’ and ‘w’ belong to a similar class, weights are ...
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A Survey on Intrusion Detection Systems and Classification Techniques
... Extreme Learning Machine (ELM) is a new emergent technology which provides good generalization performance for both classification and regression problems at highly fast learning ...Support Vector ...
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Determination of Compressive Strength of Concrete by Statistical Learning Algorithms
... [6] H. Suetani, A. M. Ideta, and J. Morimoto, “Nonlinear structure of escape-times to falls for a passive dynamic walker on an irregular slope: Anomaly detection using multi-class support vector machine and latent ...
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Detection of Neurodegenerative Disease Using Salient Brain Patterns
... Support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for ...
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Opinion Mining of M Learning Reviews using Soft Computing Techniques
... machine learning techniques for ...object. Algorithms specifically designed for opinion classification exploits opinion words and phrases together with some scoring functions ...
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ANN in Medical Field Survey Paper
... back-propagation algorithms trained feed forward neural network and fitting neural network respectively for the accuracy enhancement ...proposed learning was much faster and accurate as compared to the ...
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A DOUBLE GUARD HILL CIPHER SUITABLE FOR WIRELESS SENSOR NETWORKS
... output vector is often called a vector of references to classes declared by the ...reference vector. LVQ network after the training vector input in classifying the same class as the unit ...
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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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Analysis of Students Performance Using Data Mining
... Students’ digital footprints provide vast amount of implicit knowledge and a whole new perspective for educational researchers and practitioners to understand students’ experiences outside the controlled classroom ...
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