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Protein-protein interactions play a crucial role in the cellular process such as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine (SVM) to infer protein interactions, which can be used not only to explore all possible domain interactions by the kernel method, but also to reflect the evolutionary conservation of domains in proteins by using the domain scores of proteins. The experimental result on the Saccharomyces cerevisiae dataset demonstrates that this approach can predict protein-protein interactions with higher performances compared to the existing approaches. 相似文献
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分析了支持向量机(SVM)的工作原理和将其推广到多类分类时会遇到的问题,对用模糊SVM(FSVM)解决此问题时的模糊策略作了详细论证,说明此模糊策略是非常完美的一个解决方案,并指出了进一步的研究方向。 相似文献
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本文是为了解决在互联网上用于实时多媒体通信的RTP包的识别问题。方法是通过SVM识别RTP报头的特征来进行分类。实验表明，这种方法取得了比较好的分类效果。 相似文献
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SEETHALAKSHMI R. SREERANJANI T.R. BALACHANDAR T. Abnikant Singh Markandey Singh Ritwaj Ratan Sarvesh Kumar 《浙江大学学报(A卷英文版)》2005,6(11):1297-1305
INTRODUCTION Optical Character Recognition (OCR) deals with machine recognition of characters present in an input image obtained using scanning operation. It refers to the process by which scanned images are electroni- cally processed and converted to an editable text. The need for OCR arises in the context of digitizing Tamil documents from the ancient and old era to the latest, which helps in sharing the data through the Internet. Tamil language Tamil is a South Indian language spo… 相似文献
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Making appropriate decisions is indeed a key factor to help companies facing challenges from supply chains nowadays. In this paper, we propose two data-driven approaches that allow making better decisions in supply chain management. In particular, we suggest a Long Short Term Memory (LSTM) network-based method for forecasting multivariate time series data and an LSTM Autoencoder network-based method combined with a one-class support vector machine algorithm for detecting anomalies in sales. Unlike other approaches, we recommend combining external and internal company data sources for the purpose of enhancing the performance of forecasting algorithms using multivariate LSTM with the optimal hyperparameters. In addition, we also propose a method to optimize hyperparameters for hybrid algorithms for detecting anomalies in time series data. The proposed approaches will be applied to both benchmarking datasets and real data in fashion retail. The obtained results show that the LSTM Autoencoder based method leads to better performance for anomaly detection compared to the LSTM based method suggested in a previous study. The proposed forecasting method for multivariate time series data also performs better than some other methods based on a dataset provided by NASA. 相似文献
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分析了风力机的基本特性,阐述了风力发电机组控制系统在低于额定风速时风力机的最大风能捕获及高于额定风速情况下的变桨距控制。在此基础上,利用SVM(support vector machines)优化风力机的风能利用系数以及变桨距控制系统的控制参数。仿真分析表明,风能转换系数的支持向量机模型具有很好的精度和泛化性能,而优化后的变桨距控制系统可对输出功率的调节获得较好的效果,保证风电系统的恒功率输出。 相似文献
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对于已经分类的数据和大量未分类数据,在运算过程中,采用一种新的半监督聚类算法为支持向量机提供新的训练数据．随后,利用支持向量机判别出所有数据的类别属性,并选取最可靠的点加入已分类集合．为了验证算法的效率,收集了67张黄瓜叶片色调的数字信息,并对具有6个已分类数据与61个未分类数据的数据集进行半监督聚类分析,以判断这些叶片的健康程度．结果表明,该聚类算法优于其他算法． 相似文献
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结合Gabor小波变换的特征提取算法提出了一种基于决策模板的多分类支持向量机.该方法在对JAFFE基本表情数据库进行训练并测试时获得了较高的正确率,实验结果表明该方法是一种有效的表情识别算法. 相似文献
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