In the problem of unsupervised domain adaption Extreme learning machine(ELM), the output layer parameters need to have both classification and domain adaptation functions, which often cannot be simultaneously fully ut...
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In the problem of unsupervised domain adaption Extreme learning machine(ELM), the output layer parameters need to have both classification and domain adaptation functions, which often cannot be simultaneously fully utilized. In addition, traditional matching method based on data probability distribution cannot find the common subspace of source and target domains under large difference between domains. In order to alleviate the pressure of double functions of classifier parameters, the entire ELM learning process is mainly divided into two stages: feature representation and adaptive classifier learning, thus a joint feature representation and classifier learning based unsupervised domain adaption ELM model is proposed. In the feature representation stage, the source and target domain data are projected to their respective subspace while minimizing the difference in probability distribution between the two domains. In the adaptive classifier learning stage, the smooth manifold regularization term of target domain is used to improve the parameter adaptive *** on six different types of datasets show that the proposed model has higher cross-domain classification accuracy.
In this study, a fuzzy classification approach based on colour features has been investigated to estimate the ripeness of apple fruits according to three maturity stages;unripe, turning-ripe and ripe. The K nearest ne...
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In this study, a fuzzy classification approach based on colour features has been investigated to estimate the ripeness of apple fruits according to three maturity stages;unripe, turning-ripe and ripe. The K nearest neighbour algorithm was applied in order to segment the fruit image into four regions namely background, green area, yellow area and red area. The last three regions represent the colour features and were subsequently given as inputs to the fuzzy classifier. Gradient method has been used for tuning the fuzzy classifier in order to obtain the best performance. Image database used for simulation has been collected and exploited for the training and testing phases using cross-validation. Simulation results indicate that the best classifier parameters can be obtained. The efficiency of the proposed system compared with the non-use of the gradient method has been proved by the confusion matrix and the most known classification evaluation metrics. Moreover, the trained fuzzy classifier demonstrates its outperformance in terms of accuracy and execution time compared with other existing methods.
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