vector quantization neural networks
Improved Speech Recognition Processes Using Hybrid Genetic Vector Quantization
... Vector quantization, Neural Networks, Dynamic Time Warping, Hidden Markov Models and Genetic Algorithms and ...using Vector quantization in Pattern Recognition is its low ...
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Survey on Effective Kidney Stone Disease Diagnosis Using ANN
... different neural network algorithms which have different architecture and ...three neural networks on the basis of its accuracy, time taken to build model, and training data set ...Learning ...
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Brain MRI Classification Using PNN and Segmentation Using K Means Clustering
... Support Vector Machine (SVM), Accuracy of only 65% was ...Probabilistic Neural Network (PNN) model that is based on learning vector quantization (LVQ) with image and data analysis and ...
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A Novel Hybrid Technique for Analysis of Image Compression Metrics Using Neural Networks
... is quantization. Quantization [7,8] is the process of mapping a continuous or discrete scalar or vector produced by a source, into a set of digital symbols which can be transmitted using a finite ...
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CLASSIFICATION OF DISEASED PLANT LEAVES USING NEURAL NETWORK ALGORITHMS
... various neural network ...Forward Neural Network (FFNN), Learning Vector Quantization (LVQ) and Radial Basis Function Networks (RBF) by processing the set of shape and texture features ...
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A survey on neural network models for data analysis
... Linear Vector Quantization (LVQ) networks An LVQ network firstly has a competitive layer and then a second linear ...LVQ networks have been applied to the problem of character ...
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Image Compression using Neural Network
... a neural network based technique that may be applied to image ...a neural network based self-organizing Kohonen map technique that may be a reliable and efficient way to achieve vector ...Kohonen ...
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Vector Control of a Grid-Connected Rectifier/Inverter Using an Artificial Neural Network
... d-q vector control ...a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming (DP) algorithm and is trained using backpropagation through ...
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Comparison of Artificial Intelligence Methods on the Example of Tea Classification Based on Signals from E nose Sensors
... using neural networks, fuzzy systems, and hybrid systems like evolutionary-neural, neuro-fuzzy, ...data vector by aggregation of the first three principal components is also ...
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Image Compression Using Different Vector Quantization Algorithms and Its Comparison
... without quantization in light of the fact that a large portion of the data regularly resides in it and for a similar reason Huffman coding connected on this sub-band then RVQ connected on the detail sub-groups ...
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Deep Machine Learning In Neural Networks
... and neural network methods are the greatest important concepts in Artificial Intelligence (AI), and it produces most efficient solutions than heuristic ...recurrent neural network models and reinforcement ...
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New Intelligent Classification Techniques for Diagnosis of Diabetes Mellitus based on Modified PSO
... PSO is based on the principle that each solution can be represented as a particle in the swarm. Each particle has a position in the search space which is represented by a vector xi (xi1 , xi 2 ,..., xiD ) , where ...
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Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment
... The capability of the classifier to exploit the information in the reconstructed image is assessed primarily by the clas- sification performance. The classifier performance level de- pends on both its structure and ...
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Performance Enhancement in Lip Synchronization Using MFCC Parameters
... forward neural network with back propagation algorithm is the common choice in classification and pattern recognition ...Model, Vector Quantization are the some of the techniques for acoustic ...
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Machine learning CICY threefolds
... primitive neural network of the multi-layer perceptron type ...complex neural network or an SVM) yields significantly better ...primitive neural network in [1], and with the foresight that large ...
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Multiple-Description Multistage Vector Quantization
... assignments is clear. In particular, System A has much lower central distortion at low-loss probabilities, compared to Sys- tem B and System C. This is primarily due to the use of rep- etition codes for the first stage ...
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TAG Parsing with Neural Networks and Vector Representations of Supertags
... The model architecture we adopt is depicted in Figure 3, a BLSTM. The input for each word is represented via the concatenation of a 100-dimensional embedding of the word, a 5- dimensional embedding of a predicted part of ...
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Determination of Corrosion Types from Electrochemical Noise by Artificial Neural Networks
... Before training BP neural networks, the number of hidden layers has to be determined. The input has 10 elements, so 9 hidden layers is suitable The training function provided by matlab is used to train ...
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Speaker Identification by using Vector Quantization
... Fig. 3 shows the generation of 4 code vector codebook for the 16 dimensional vector space using the LBG algorithm. The code vectors thus formed are the feature vectors used in the training phase. Fig. 3(A) ...
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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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