gradient post

gradient post的基本解释

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坡度标桩

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坡度标

与gradient post相关的网络例句 [注：此内容来源于网络，仅供参考]

In Salsola passerina -Reaumuria soongorica community, the first ordination axes explains the salinization gradient, along the order of Caragana tibetica community,Salsola passerina-Oxytropis aciphylla community,and Reaumuria soongorica-Salsola passerine community,soil alkalization increases. The second ordination axis explains soil structure gradient, along the order of Caragana tibetica community, Reaumuria soongorica-Salsola passerina community and Salsola passerina community, soil texture becomes coarser. In the Stipa breviflora-Stipa grandis community, the first ordination axis indicates the soil water gradient, and the second ordination axes explains hydrothermal coupling gradient. In the Prunus mongolica-Ulmus glaucescens community, the first ordination axis explains the soil pH gradient, along the order of Ulmus glaucescens-Prunus mongolica community, Prunus mongolica-Potentilla fruticosa community, and Potentilla fruticosa-Prunus mongolica community, soil pH value reduces. The second ordination axis explains soil structure gradient, along the order Prunus mongolica-Ulmus glaucescens community, and Prunus mongolica-Potentilla fruticosa community, the contents of silt and clay increase, and soil texture suggests a fine trend.

在珍珠猪毛菜-红砂群落，第一排序轴反映了土壤盐碱化梯度，沿着藏锦鸡儿群落—珍珠猪毛菜、猫头刺群落—珍珠猪毛菜、红砂群落序列，土壤盐碱化程度不断增强；第二排序轴则反映了土壤结构梯度，沿着藏锦鸡儿群落—珍珠猪毛菜、红砂群落—珍珠猪毛菜、猫头刺群落序列，土壤质地逐渐粗化；在短花针茅-大针茅群落，第一排序轴反映了土壤水分梯度，第二排序轴反映了海拔梯度上的水热组合梯度；在蒙古扁桃-灰榆群落，第一排序轴反映了土壤pH梯度，沿着灰榆、蒙古扁桃群落—蒙古扁桃、金露梅群落—蒙古扁桃群落序列，土壤pH值逐渐下降；第二排序轴主要反映了土壤结构梯度，沿着蒙古扁桃群落—灰榆、蒙古扁桃群落—蒙古扁桃、金露梅群落序列，土壤中粉粒、粘粒含量逐渐增加，土壤质地呈细化趋势。

In Salsola passerina-Reaumuria soongoriea community, the first ordination axes explains the salinization gradient. along the order of Caragana tibetica community, Salsola passerina-Oxytropis aciphylla community, and Reaumuria soongorica-Salsola passerine community, soil alkalization increases. The second ordination axis explains soil structure gradient, along the order of Caragana tibetica community, Reaumuria soongorica-Salsola passerina community and Salsola passerina community, soil texture becomes coarser. In the Stipa breviflora-Stipa grandis community, the first ordination axis indicates the soil water gradient, and the second ordination axes explains hydrothermal coupling gradient. In the Prunus mongolica-Ulmus glaucescens community, the first ordination axis explains the soil pH gradient, along the order of Ulmus glaucescens-Pnuius mongolica community, Prunus mongolica-Potentilla fruticosa community, and Potentilla fnuicosa-Prunus mongolica community, soil pH value reduces. The second ordination axis explains soil structure gradient, along the order Prunus mongolica-Ulmus glaucescens community, and Prunus mongolica-Potentilla fruticosa community, the contents of silt and clay increase, and soil texture suggests a fine trend.

在珍珠猪毛菜－红砂群落，第一排序轴反映了土壤盐碱化梯度，沿着藏锦鸡儿群落－珍珠猪毛菜、猫头刺群落－珍珠猪毛菜、红砂群落序列，土壤盐碱化程度不断增强；第二排序轴则反映了土壤结构梯度，沿着藏锦鸡儿群落－珍珠猪毛菜、红砂群落－珍珠猪毛菜、猫头刺群落序列，土壤质地逐渐粗化；在短花针茅－大针茅群落，第一排序轴反映了土壤水分梯度，第二排序轴反映了海拔梯度上的水热组合梯度；在蒙古扁桃－灰榆群落，第一排序轴反映了土壤pH梯度，沿着灰榆、蒙古扁桃群落－蒙古扁桃、金露梅群落－蒙古扁桃群落序列，土壤pH值逐渐下降；第二排序轴主要反映了土壤结构梯度，沿着蒙古扁桃群落－灰榆、蒙古扁桃群落－蒙古扁桃、金露梅群落序列，土壤中粉粒、粘粒含量逐渐增加，土壤质地呈细化趋势。

Among used machine learning methods, the gradient descent method is widely used to train various classifiers, such as Back-propagation neural network and linear text classifier. However, the gradient descent method is easily trapped into a local minimum and slowly converges. Thus, this study presents a gradient forecasting search method based on prediction methods to enhance the performance of the gradient descent method in order to develop a more efficient and precise machine learning method for Web mining.However, a prediction method with few sample data items and precise forecasting ability is a key issue to the gradient forecasting search method. Applying statistic-based prediction methods to implement GFSM is unsuitable because they require a large number of data items to model a prediction model. In the contrast with statistic-based prediction methods, GM(1,1) grey prediction model does not need a large number of data items to build a prediction model, and it has low computational load. However, the original GM(1,1) grey prediction model uses a mathematical hypothesis and approximation to transform a continuous differential equation into a discrete difference equation in order to model a forecasting model.

其中梯度法是一个最常被使用来实现机器学习的方法之一，然而梯度法具有学习速度慢以及容易陷入局部最佳解的缺点，因此，本研究提出一个梯度预测搜寻法则(gradient forecasting search method， GFSM)来改善传统梯度法的缺点，用来提升一些以梯度学习法则为基础的分类器在资讯探勘上的效率与正确性；而一个所需资料量少、计算复杂度低且精确的预测模型是梯度预测搜寻法能否有效进行最佳解搜寻之关键因素，传统统计为基础之预测方法的缺点是需要较大量的数据进行预测，因此计算复杂度高，灰色预测模型具有建模资料少且计算复杂度低等优点，然而灰色预测理论以连续之微分方程式为基础，并且透过一些数学上的假设与近似，将连续之微分方程式转换成离散之差分方程式来对离散型资料进行建模及预测，这样的作法不尽合理，且缺乏数学理论上的完备性，因为在转换过程中已经造成建模上的误差，且建模过程仅考虑相邻的两个资料点关系，无法正确反应数列未来的变化趋势。