搜索资源列表
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支持向量机用于实验数据拟合分类优化,速度快,Support Vector Machines for classification optimization of the experimental data fitting, fast
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用于分类的一个工具箱，内含支持向量机、神经网络、主元分析、多变量样条等方法的matlab源代码，还有用户手册。-A tool for classification, including support vector machines, neural networks, principal component analysis, multivariate methods such as spline matlab source code, as well as user manuals.
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一个毕业程序设计，是用小波和支持向量机对电能质量扰动的识别与分类，主要利用的能量特征。-A graduate program design is the use of wavelet and support vector machines for power quality disturbance recognition and classification, the main characteristics of energy use.
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一篇关于支持向量机在故障诊断方面的优秀博士论文，值得参考此文献。-An article on Support Vector Machines in Fault Diagnosis of the outstanding aspects of the doctoral thesis, it is also useful in this literature.
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有关支持向量机的讲座,我看了之后的确受益非浅-Support Vector Machines on the talks, after I read indeed benefited
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支持向量机，matlab工具集。涵盖关于支持向量机的各种主要算法实现。-Support Vector Machines, matlab tools. Support Vector Machine on the cover of the main algorithm.
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Support Vector Machines Ensemble and its Application in Remote Sensing Classification支持向量机在遥感图像中的应用-Support Vector Machines Ensemble and its Application in Remote Sensing Classification
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利用支持向量机对T-S型模糊系统建模的方法，结合BP算法对参数进行优化，从一定程度上解决模糊系统建模所存在的模型结构复杂、维数灾、泛化能力不强和实时性差等问题。-this paper analyzed the approach that applied support vector machines to create novel model in the T-S fuzzy system, combined with BP algorithm which could optimize it,
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用于在matlab中实现非线性回归的支持向量机svm算法-Used matlab to implement non-linear regression algorithm of support vector machines svm
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模糊——支持向量机，用于模糊理论与支持向量机结合，用于数据预测-Fuzzy- support vector machines for fuzzy theory and support vector machines for data predict
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支持向量机的一些常用的源代码，包括KPCA，KFD等。-Some commonly used support vector machines source code, including KPCA, KFD and so on.
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一篇介绍将SVM应用于人脸探测的论文，推荐-Training support vector machine an application to face detection
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Support vector machines for classification and regression
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support vector machines
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改进的P_SVM支持向量机与遥感数据分类,对多光谱高光谱分类-Improved P_SVM support vector machines and remote sensing data classification, Classification of multispectral hyperspectral
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支持向量机模型研究及应用，本文所做的工作主要在如下几个方面：
1)运用模糊集理论(FST)和概率理论对支持向量机进行研究，构造出了概率模糊支
持向量机(PFSVM)模型，既达到了减少或者消除外围异样点对于整个训练模型的影响，-Support vector machine model and application, this work mainly in the following aspects: 1) the use of fuzzy set theory (FST) and the
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Support Vector Machines
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支持向量机（SVM）的实现代码和步骤，共包括五个步骤，每一步都有详细的说明。-Support Vector Machine (SVM) implementation code and procedures, including a total of five steps, each step in detail.
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Classification of breast tissues based on Support Vector Machines
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svm support vector machines
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