learning vector quantization method
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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Semi-Supervised Learning Vector Quantization method enhanced with regularization for anomaly detection in air conditioning time-series data
... We compare SSL-RPC performance to other semi-supervised and unsupervised models. The synthetic data was generated using the same technique as in the T1-T2 experiment. We run the model on 25 different sizes of the labeled ...
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Transfer learning through policy abstraction using learning vector quantization
... former method, the number of abstract policy generated using only modified LVQ is almost double and the accumulative steps are 341 steps ...proposed method, the agent successfully learned to complete the ...
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Fuzzy Modeling using Vector Quantization based on Input and Output Learning Data
... effective learning algorithm using the probability of output change for learning data compared to other methods and consider to apply the proposed method to learning of neural ...
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Dropout in Learning Vector Quantization Networks for Regularized Learning and Classification Confidence Estimation
... metric learning has yielded tremendous improvements in classification accuracy and can be considered a standard method [1, ...metric learning for edit distances is complicated my multiple ...
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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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Learning Vector Quantization (LVQ) Neural Network Approach for Multilingual Speech Recognition
... using Learning Vector Quantization (LVQ) Neural Network and Particle Swarm Optimization (PSO) ...This method is applied into two phases, in the first phase MFCC and LPC feature extraction ...
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Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms
... The algorithm defined by the iterations (3) is known as the CLVQ algorithm in the data analysis community. It is also called the Kohonen Self Organizing Map algorithm with 0 neighbor (see for instance Kohonen 1982) or ...
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Brain MRI Classification Using PNN and Segmentation Using K Means Clustering
... In method used by Mohd Fauzi Bin Othman, et al[6], performed classification of brain tumor using wavelet based feature extraction method and Support Vector Machine (SVM), Accuracy of only 65% was ...
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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 ...
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Improving learning vector quantization using data reduction
... The method can outperform the original condensed nearest neighbor and give more consistent results under certain ...The method can also determine the optimal number of clusters by deleting noisy data and ...
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Image Contrast Enhancement using Learning Vector Quantization
... They presented an advanced Histogram Equalization algorithm for contrast Enhancement .Global Histogram Equalization is simple and fast but its contrast enhancement power is relatively low. Local histogram enhancement is ...
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Fast Learning Algorithm for Fuzzy Inference Systems using Vector Quantization
... that learning methods of fuzzy in- ference systems using vector quantization (VQ) and steepest descend method (SDM) are superior in terms of the number of ...a learning method of ...
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Combining dissimilarity measures for prototype-based classification
... a Learning Vector Quantization classification scheme for heterogeneous, mixed ...the method we apply it to diagnosing viral crop disease in cassava plants from histograms (HSV) and shape ...
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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 vector ...
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Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches
... [8] R. Hu, S. J. Delany, and B. Mac Namee. Sampling with Confidence: Using k-NN Con- fidence Measures in Active Learning. In Proceedings of the UKDS Workshop at 8th International Conference on Case-based ...
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Average Competitive Learning Vector Quantization
... Searching an optimal quantizer is one of the main interest on this frame- work. Only for a few cases the close form is known. It is posible to compute the optimal quantizer at level n in one dimension as the solution of ...
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Distance Learning in Discriminative Vector Quantization
... vided in Fig. 4. The diagonal elements turn out that the algorithm totally eliminates the noisy dimensions 4 to 10, which, in consequence, do not contribute to the computation of distances any more. As reflected by the ...
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Spoken Word Recognition Using MFCC and Learning Vector Quantization
... MFCC is popular because the efficient extraction method with its robustness in presence of different noises. In MFCC stage, the speech signal is passed through several triangular filters which are spaced linearly ...
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A survey on neural network models for data analysis
... A single layer perceptron network is the oldest one, which consists of a single layer outputs via a series of weights. And therefore it is considered the simplest kind of feed-forward network. The sum of the products of ...
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