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Furthermore, we have selected and employed the neural network based on learning vector quantization for correct classification.
Snap is based on a modified form of Adaptive Resonance Theory (CGIP 37 1987 54); and drift is based on Learning Vector Quantization (LVQ) (Proc. IJCNN 1(1990a)545).
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Support vector machine (SVM) is firmly based on learning theory and uses regression technique by introducing accuracy insensitive loss function.
Additionally, the rewards-punishments training process was improved for a selected neural network, which is based on a Learning Vector Quantization (LVQ) algorithm.
To enhance this task, the proposed method was developed based on a learning vector quantitative neural network (LVQNN) algorithm.
This paper pays more attention to the estimation of travel time for left-turning lane connected to an intersection, introduces the features for travel time estimation, and designs an estimator based on the learning vector quantity (LVQ) neural network.
We propose a new adaptive active clustering scheme, based on an initial fuzzy c-means clustering and learning vector quantization.
Data modeling techniques based on machine learning such as support vector machines (SVMs) can partially reduce workload, aid clinical decision-making, and lower the frequency of human error [ 4].
The new regression model is based on support vector machines learning technique, which is an extension of Statistical Learning Theory.
In this paper, support vector machine approach was proposed to overcome these limitations based on statistics learning theory, and a new fault diagnosis system is developed.
Machine learning methods based on support vector machines have been investigated in several projects [17], [18].
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