brain-computer interfaces; electroencephalography; operating system kernels; optimisation; pattern classification; support vector machines; EEG signal; LOOCV cost function; LS-SVM parameter; MI electroencephalogram signal; automatic classifier tuning; brain-computer interface system; classification accuracy; classification speed; classifier optimum configuration; coupled simulated annealing; kernel model parameter; least-square support vector machine; leave-one-out cross validation; mental task classification; multiclass self-paced motor imagery temporal feature classification; optimization technique; sign slop change feature; simplex; Accuracy; Electroencephalography; Feature extraction; Kernel; Support vector machines; Training; Tuning; BCL; EEG temporal features; Self-paced motor imagery; classification; least-square support vector machine;
机译:基于纹理特征提取和多类支持向量机的异步电动机可靠故障分类
机译:用于模拟调制分类的多类最小二乘支持向量机
机译:二元和多标准电动机图像分类的EEG空间,光谱和颞座中提取的特征的比较分析
机译:使用最小二乘支持向量机的多类自定步运动图像时域特征分类
机译:使用支持向量机分类和基于区域的对象拟合,将LiDAR与航空影像融合以估计砍伐的树木量。
机译:鲁棒的稀疏表示和多类支持矩阵机用于运动图像脑电信号的分类
机译:基于颜色特征的可可豆数字图像分类使用多碳组合最小二乘支持向量机