2) mean square error 均方误差 1. Firstly,screen the descriptors using support vector machine regression(SVR) by leave-one-out method based on the minimum mean square error(MSE),get the optimal kernel and the corresponding retained descriptors. 首先以均方误差(MSE)最小为原则,以留一法通过多轮...
给定一个视频序列,在视频的第一帧时框选出需要跟踪的目标,则后续视频播放过程中,目标框可以跟踪目标的位置。 2.MOSSE算法是最初将相关滤波应用于目标跟踪领域中的,它在保持较好的跟踪效果的同时,其速度远超同时期的其它跟踪算法,最快能达到669FPS。 3.MOSSE算法的基本思想:首先根据第一帧图像框选的目标构建一个...
MulCom Sum of Square ErrorClaudio Isella
testing a 1st order system and trying to determine the time constant by fitting to a sinusoidal chirp function. I'm using the system identification tool to fit the sinusoidal output and determine the time constant. What I need to get is the sum of square error. Is there a way to do ...
水平项误差平方和: sum of squares for factor A 误差向平方和: sum of squares for error 均方: mean square …jg.qust.edu.cn|基于3个网页 2. 组内平方和 在统计学中,组内平方和(sum of squares for error)简记为SSE,表示每个水平或组的各样本数据与其组均值的误差平方和,反映 …baike.baidu.com|...
aStandard Deviation,也称mean square error,是各数据偏离平均数的距离的平均数,它是离均差平方和平均后的方根,用σ表示。标准差是方差的算术平方根。标准差能反映一个数据集的离散程度。平均数相同的,标准差未必相同。 Standard Deviation, also called mean square error, is various data deviation mean value dis...
残差平方和;sum of square of... ... 5) 误差; error 1) 误差平方和; error sum of square 2) 残差; residual ... chemyq.com|基于4个网页 2. 平方误差 沃德误差法与其它 聚类分析的不同在于它把平方误差(Error sum of square)作为样本信息损 失的标准,聚类过程中每一步的归并以 …lantianyu.net|...
1)square sum of error误差平方和 英文短句/例句 1.SOME NEW RESULTS ON VALUE RANGE OF OPTIMAL COMBINATION FORECASTING ERROR SQUARE SUM;最优组合预测误差平方和取值范围的若干新结果 2.The results reveal that the combined forecasting model is more effective.结果表明,此组合预测平均误差和预测平方根误差均较...
3) sum square error 误差平方和法4) sum-of-squared-error criterion 误差平方和准则 1. In this paper,we propose an unsuperviset strategy(UKNNC)for the KNN Classifier,which adopts sum-of-squared-error criterion to adaptively select the contributing part from these neighbors and classifies the ...
Square your results from Step 3: -2 x -2 = 4 0 x 0 = 0 2 x 2 = 4 Sum (add up) all of your numbers: 4 + 4 + 0 = 8. That’s it! The higher the sum of squares, the more variation in the data. This can be useful in comparing different data sets. ...