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The way that skill varies with season and the Southern Oscillation Index is consistent with other research but not statistically significant for this small data set.


This data set was then compared with the small data set presented by a company for benchmarking.


The opposite extreme is to select a small data set, thereby being able to learn very expressive (firstorder logic) hypotheses.


Following this string of ideas leads to the discovery that the Bergman kernel can be "zipped" down to a strikingly small data set.


As an application fordemonstration, a small data set from a garigue community on ultramafic soils ofTuscany (central Italy) is used.

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 A comparison of two methods for estimation of variance component,MIVQUE and KEML,was carried out on four data sets with Monte Carlo simulation. The model used was the sire model containing herdyearseason(HYS) effect(fixed),sire group effect(fixed)and sire effect(random), which is widely used in dairy cattle breeding. The largest data set consisted of 12847 records with 47 sires and 778 HYSs, which is corresponding to the milk yield data available currently in Beijing area. The smallest... A comparison of two methods for estimation of variance component,MIVQUE and KEML,was carried out on four data sets with Monte Carlo simulation. The model used was the sire model containing herdyearseason(HYS) effect(fixed),sire group effect(fixed)and sire effect(random), which is widely used in dairy cattle breeding. The largest data set consisted of 12847 records with 47 sires and 778 HYSs, which is corresponding to the milk yield data available currently in Beijing area. The smallest data set comprised 200 records with 148 HYSs and 20 sires. The criterion for the comparison were the bias and variance, either theoretical or empirical based on 1000 repeated simulations, of the estimates. The results show that for the larger data sets the two methods are little different from each other:Bias<1% of the true values,correlation≈1,and variance(MIVQUE)≈variance(REML).For the smaller data sets the MIVQUE was significantly better than REM L.It was also shown that for REML the sample size like the first data set can satisfy its large sample properties of asymptotical unbiasedness and efficiency.  利用ＭｏｎｔｅＣａｒｌｏ方法，对４种数据结构进行了ＭＩＶＱＵＥ和ＲＥＭＬ两种方差组分估计方法的模拟比较。方差组分估计所用的模型为奶牛育种中常用的公畜模型，它包括场年季固定效应、公牛组固定效应和公牛随机效应。４种数据结构中最大的有１２８４７个观察值，场年季效应和公牛效应水平数分别为７７８和４７，它与北京市目前可利用的奶牛头胎产奶量记录资料相当。最小的数据结构只有２００个观察值，１４８个场年季和２０头公牛。比较指标为估计值的偏差和方差（理论的或根据１０００次重复模拟所得的经验值）。结果表明，对于较大样本的数据结构，两种方法差异很小，它们间的估计值的相关接近于１，偏差小于真值的１％，方差近似相等。对于较小样本的数据结构，ＭＩＶＱＵＥ则明显优于ＲＥＭＬ。本研究还表明，对于ＲＥＭＬ来说，类似数据结构１的样本已能满足其渐近无偏性和有效性的大样本特性。  Objective The method of threedimension reconstruction with small data set was presented and then was used to investigate location and shape of the brain nucleus.Methods Twenty adult whole brains from cadavers without gross pathologic changes were collected and were cut into a series of slices of 2 mm in thickness. The red nucleus, lentiform nucleus were reconstructed with the method based on surface modeling method. The volume and location of these reconstructed nuclei were measured by programs on computer.... Objective The method of threedimension reconstruction with small data set was presented and then was used to investigate location and shape of the brain nucleus.Methods Twenty adult whole brains from cadavers without gross pathologic changes were collected and were cut into a series of slices of 2 mm in thickness. The red nucleus, lentiform nucleus were reconstructed with the method based on surface modeling method. The volume and location of these reconstructed nuclei were measured by programs on computer. Results The brain nuclei were reconstructed and data about shape and location of reconstructed brain nucleus was acquired. Conclusion The three dimension reconstruction method presented by this paper is effective to reconstruct brain nuclei and is helpful to identify precisely shape and location of brain nuclei.  目的 探索小数据量条件下三维重建方法,并用之研究脑内神经核团的空间形态和位置.方法 以20只整脑2mm厚的连续切片为数据源,运用曲面造型法,重建豆状核、红核等脑内神经核团的三维形状.结果 在微机上重建出脑内神经核团的形状,并获得核团中心位置、体积等数据.结论 三维重建方法对脑内神经团的三维重建切实有效,有助于对脑内神经核团形态位置的精确研究.  An identification method of nonlinear systems using least squares support vector machine(LSSVM) is proposed. The constraints of inequalities in the classical SVM approach are replaced by equalitytype constraints in LSSVM. The LSSVM solution follows directly from solving a set of linear equations instead of quadratic programming. A kind of nonlinear predictive control scheme based on the LSSVM model is presented. Simulation results for a CSTR process show that LSSVM can be trained fastly. The LSSVM has... An identification method of nonlinear systems using least squares support vector machine(LSSVM) is proposed. The constraints of inequalities in the classical SVM approach are replaced by equalitytype constraints in LSSVM. The LSSVM solution follows directly from solving a set of linear equations instead of quadratic programming. A kind of nonlinear predictive control scheme based on the LSSVM model is presented. Simulation results for a CSTR process show that LSSVM can be trained fastly. The LSSVM has good ability of modeling nonlinear process and good generalization under small data set available. The nonlinear predictive control strategy based on LSSVM model shows satisfactory performance.  探讨了利用最小二乘支持向量机(LSSVM)进行非线性系统辨识的方法,LSSVM用等式约束代替传统支持向量机中不等式约束,求解过程从解QP问题变成解一组等式方程.将得到的LSSVM模型应用到非线性预测控制,提出了基于LSSVM模型的非线性预测控制算法.通过CSTR过程仿真表明,最小二乘支持向量机学习速度快,在小样本情况下具有良好的非线性建模和泛化能力.基于LSSVM的预测控制算法具有很好的控制性能.   << 更多相关文摘 
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