Cluster Discriminant Prediction of Oil Well Production Based on Mahalanobis Distance

Character Recognition with Mahalanobis Distance Based on Between-cluster Information

In the case of using the Mahalanobis distance as discriminant function, usually the covariance matrix calculated from training samples is used. However, it is extremely difficult to prepare enough training samples if the dimension of feature vector is large. Therefore, estimated eigenvalues and eigenvectors of covariance matrix will include errors that cause misclassification. In this paper, a ...

Normalized Clustering Algorithm Based on Mahalanobis Distance

FCM (fuzzy c-means algorithm) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. The added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. In this paper, an improved Normalized Clustering Algorithm Based on Mahalanobis distance by t...

Anisotropic Superpixel Generation Based on Mahalanobis Distance

Superpixels have been widely used as a preprocessing step in various computer vision tasks. Spatial compactness and color homogeneity are the two key factors determining the quality of the superpixel representation. In this paper, these two objectives are considered separately and anisotropic superpixels are generated to better adapt to local image content. We develop a unimodular Gaussian gene...

Predicting the Oil Well Production Based on Multi Expression Programming

Predicting the oil well production is very important and also quite a complex mission for the petroleum engineering. Due to its complexity, the previous empirical methods could not perform well for different kind of wells, and intelligent methods are applied to solve this problem. In this paper the multi expression programming (MEP) method has been employed to build the prediction model for oil...

Mahalanobis distance based on fuzzy clustering algorithm for image segmentation