The second approach is a novel core-to-log workflow using dimensionality reduction techniques applied to core data to capture the inherent correlations between minerals. Different mineral assemblages are described, and the learning is extended to the definition of associated end points for cased hole ...
In order to increase the accuracy of a model, one can apply a finer computational grid in space or time, or use a more detailed description of the fluids such as in thermal-compositional model. However, the improvement in the accuracy of models is usually counterbalanced by the reduction in...
Notebook_8_dim_red_and_clustering_of_feature_importances.ipynb - This notebook in Python clusters reservoir based on their similarities in the feature importance space. The feature importance space is first reduced using various dimensionality reduction methods. The notebook implements different clusteri...
A. Clustering data B. Regression analysis C. Classification of data D. Dimensionality reduction 相关知识点: 试题来源: 解析 C。支持向量机(SVM)主要用于数据的分类。它通过寻找一个超平面来将不同类别的数据分开。聚类数据通常由聚类算法完成,回归分析由回归算法完成,降维由主成分分析等方法完成。反馈...
However, general purpose software are much more flexible and their advantages for the user are manifold, mainly the possibility to solve different types of problems adopting a single software. This results in a drastic reduction in the efforts that an analyst needs to invest to familiarize with ...
dimensional data can be converted to low-dimensional codes by training the model such asStacked Auto-EncoderandEncoder/Decoderwith a small central layer to reconstruct high-dimensional input vectors. This function of dimensionality reduction facilitates feature expressions to calculate similarity of each ...
Matrix specific yield (Sy) has been considered as an adjustable parameter in the preliminary models. However, it was entirely “insensitive” in the course of inversion of all variants, and therefore, excluded to reduce the inversion dimensionality. A fixed value of 0.05 was considered for the ...
For a model with a high number of input parameters and significant parameter interactions, Ziehn and Tomlin recommend first applying a screening method such as the Morris method to reduce the dimensionality of the problem and thus improve the accuracy of the estimation of high order component functi...
To facilitate the training of LSTM-based summarisation techniques, an embedding layer is learned to reduce the dimensionality of the video features (Zhao, Li, & Lu, 2021a). Similar ideas rely on comparing original videos and their summaries in terms of embeddings (Zhang, Grauman, & Sha, 2018...
On the other hand, the look-ahead information during dynamic scheduling is finite (meaning that we do not have access to the outcome of all future disturbances) and the dimensionality of state space is high (often hundreds to thousands). Therefore, it is unlikely to obtain, or even ...