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- A new bearing fault detection approach based on relevance vector machine (RVM) is presented. 摘要针对轴承故障检测问题,提出一种基于相关向量机(RVM)的故障检测方法。
- A new regression algorithm of an adaptive reduced relevance vector machine is proposed to estimate the illumination chromaticity of an image for the purpose of color constancy. 摘要提出了一种新的自适应约简相关向量机回归算法来估计图像的光照色度以达到色彩一致性目的。
- The Relevance vector machine (RVM) is a state-of-the-art machine learning algorithm in which the training procedure is in the framework of Bayesian. 摘要将小波分析与相关向量机结合,提出了一种新的机器学习方法-小波相关向量机。
- Finally, Multi-Variate Relevance Vector Machine (MVRVM) is exploited to learn a single mapping and ensure the sparsity of regression model. 引入了多变量相关向量机,学习单个映射函数,保持回归模型的稀疏性。
- Relevance vector machine ( RVM ) 相关向量机
- Relevance vector machine for regression 相关向量机回归
- Estimation of illumination chromaticity via adaptive reduced relevance vector machine 基于自适应约简相关向量机的光照色度估计
- relevance vector machine (RVM) 相关向量机
- relevance vector machine(RVM) 相关向量机(RVM)
- relevance vector machine 相关向量机
- The Application of Relevance Vector Machines to Microbiological Fermentation Sensor Fault Diagnosis 关联向量机在微生物发酵传感器故障诊断中的应用
- BVM(Ball Vector Machine)is a faster machine learning algorithm than SVM. 球向量机是一种比SVM更快的机器学习方法。
- Combining sparse Bayesian learning with the principle of the support vector tracking(SVT), the relevance vector tracking (RVT) is presented. 结合稀疏贝叶斯学习方法和支持向量跟踪(SVT)原理,提出了相关向量跟踪(RVT)。
- Wavelet and support vector machine are used to classify the audio clips. 使用小波变换和支持向量机的方法对音频进行分类。
- In the pattern recognition strut vector machine, may use in the person face recognition training! 详细说明:模式识别中的支撑向量机,可用于人脸识别训练!
- New sentences are then detected with Winnow andsupport vector machine classifiers, respectively. 然后,我们分别采用Winnow与支持向量机分类器检测出新的句子。
- The uploaded Support Vector Machine (SVM) Classifier can classify text-type data well. 详细说明:支持向量机分类器(可分类文本,编的非常不错)
- Support Vector Machine (SVM) , which based on Statistic Learning Theory, has the adaptive generation ability. 建立在统计学习理论基础之上的支持向量机具有和样本数相适应的最优泛化能力。
- For this reason, several important properties of kernel function are discussed on the basis of support vector machine. 为此,在研究支持向量机的基础上,给出了核函数的若干重要性质。
- The hyperplane revises the error of the standard Support Vector Machines. 该分类面校正了标准的支持向量机的分类误差。