hybrid learning vector quantization
Text Dependent Multilingual Speaker Identification using Learning Vector Quantization and PSO GA Hybrid Model
... prototype vector which allows it to classify a region of the input ...input vector and any of the weight vectors is measured by the smallness of the Euclidean distance between ...
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Brain MRI Classification Using PNN and Segmentation Using K Means Clustering
... Support Vector Machine (SVM), Accuracy of only 65% was ...A Hybrid Approach for Automatic Classification of Brain MRI Using Genetic Algorithm and SVM is ...on learning vector ...
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Learning Vector Quantization (LVQ) Neural Network Approach for Multilingual Speech Recognition
... [3], hybrid ANN/HMM [4] approach is also used for speech recognition. Hybrid ANN/HMM model is the combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), TDNN and ANN/HMM is applied ...
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Improved Speech Recognition Processes Using Hybrid Genetic Vector Quantization
... Vector quantization is dependent on the aggressive learning paradigm, so that it is closely pertaining to the self-organizing map model and to sparse programming models utilized in deep studying ...
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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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Opinion Mining of M Learning Reviews using Soft Computing Techniques
... Topic related words are the key to classify documents into predefined topic classes, e.g., sports, art etc in text classification. Whereas, in opinion classification, topic-related words are not very important but, ...
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Survey on Effective Kidney Stone Disease Diagnosis Using ANN
... used Learning vector quantization (LVQ), two layers feed forward perceptron trained with back propagation training algorithm and Radial basis function (RBF) networks for diagnosis of kidney stone ...
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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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Local Identification of Overcomplete Dictionaries
... dictionary learning has been on the development of ...of learning algorithms, that perform well in experiments and are popular in applications (Field and Olshausen, 1996; Kreutz- Delgado and Rao, 2000; ...
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ANN in Medical Field Survey Paper
... Dr. K. Nageswara Rao et al. (2014) enhanced the training procedure of the neural network to diagnose the heart disease effectively, they used a hybrid algorithm which was combination of GSO and ABC. Initially, ...
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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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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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Speaker Identification by using Vector Quantization
... Very simple techniques based on the lossy compression using vector quantization have been introduced. As can be seen from the results, approach A with overlap gives better results than without overlap. Also ...
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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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Vector Quantization of Microarray Gene Expression Data
... column (fields, attributes, samples, or conditions) [2]. Datasets of breast cancer (Hedenfalk et al, 2001), sugarcane, Mus musculus, A. thaliana (all of NCBI, 2002) and yeast (Eisen et al, 1998) were used for carrying ...
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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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Codebook Generation for Vector Quantization using Interpolations to Compress Gray Scale Images
... The image compression [1] techniques are broadly classified into two categories: 1.Lossless techniques and 2.Lossy techniques. In lossless compression techniques, the reconstructed image will be the exact replica of the ...
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CLASSIFICATION OF DISEASED PLANT LEAVES USING NEURAL NETWORK ALGORITHMS
... Feature extraction is a superior form of dimensionality reduction. When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant then the input data will be transformed ...
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Data Mining with Computational Intelligence Lipo Wang , Xiuju Fu pdf
... An additional criterion is ‘the fidelity’ [286]. ‘The fidelity’ is defined as the ratio between correctly classified outputs from rule extraction and correctly classified outputs from the corresponding classifier. ‘The ...
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Introduction to vector quantization and its applications for numerics*
... optimal quantization rates, some- times sharp, can be established in this framework for various classes of stochastic processes, including the (fractional) Brownian motion and the Brownian bridge, scalar Brownian ...
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