Among classic algorithms of data mining, the K-nearest neighbor based methods are simple and effective pattern classification algorithms. However, most KNN-based methods do not fully take into account the impact of di...
详细信息
ISBN:
(纸本)9781450384155
Among classic algorithms of data mining, the K-nearest neighbor based methods are simple and effective pattern classification algorithms. However, most KNN-based methods do not fully take into account the impact of different training sample points on classification, lead to inaccurate classification. To address this issue, we propose a scheme named Attention-based local mean K-Nearest Centroid Neighbor Classifier (ALMKNCN), combining nearest centroid neighbor with attention mechanism, the influence of each training sample on the query sample is fully considered. Given the query pattern, we first calculate the local centroid mean vector for each class, and then use the idea of attention mechanism to calculate the weight of pseudo-distance between each class and test sample. Finally, based on attention coefficient, the distances between the query sample and local mean vectors are weighted to determine the class of the query sample. Extensive experiments on UCI and KEEL data sets are carried out by comparing ALMKNCN to the state-of-art KNN-based methods. the experimental results demonstrate that the proposed ALMKNCN outperforms the related competitive KNN-based methods with more effectiveness.
In the deep texture image retrieval, to address the problem that the retrieval performance is affected by the lack of sufficiently large texture image dataset used for the effective training of deep neural network, a ...
详细信息
ISBN:
(纸本)9781450384155
In the deep texture image retrieval, to address the problem that the retrieval performance is affected by the lack of sufficiently large texture image dataset used for the effective training of deep neural network, a deep learning based texture dataset construction and texture image retrieval method is proposed in this paper. First, a large-scale texture image dataset containing rich texture information is constructed based on the DTD texture image dataset, and used as the source dataset for pre-training deep neural networks. To effectively characterize the information of the source texture dataset, a revised version of the VGG16 model, called ReV-VGG16, is adaptively designed. then, the pre-trained ReV-VGG16 model is combined withthe target texture image datasets for the transfer learning, and the probability values of the output from the classification layer of the model are used for the computation of the similarity measurement to achieve the retrieval of the target texture image dataset. Finally, the retrieval experiments are conducted on four typical texture image datasets, namely, VisTex, Brodatz, STex and ALOT. the experimental results show that our method outperforms the existing state-of-the-art texture image retrieval approaches in terms of the retrieval performance.
«…to progress the state of the art of chemical and biological measurement science and act as a forum for the exchange of information about measurement research, technical programs and service delivery…»Con...
«…to progress the state of the art of chemical and biological measurement science and act as a forum for the exchange of information about measurement research, technical programs and service delivery…»Consultative Committee for Amount of Substance; Metrology in Chemistry and Biology (CCQM): Strategy Document (2021-2030)Today, more than ever, the problems of ensuring international traceability in the field of physicochemical measurements, both for science and production, and for monitoring the quality and safety of life, are relevant. the concept of “measured once, accepted everywhere” has become a cornerstone for metrologists over the world. To implement this concept, metrological institutes hold conferences, seminars, forums and other events providing participants withthe opportunity to receive information about new achievements, exchange experience between laboratories and identify problems arisen in *** of Images, Acknowledgements, Program Committee are available in this pdf.
暂无评论