Accurate identification of lithology is the basis and key process fine logging interpretation evaluation. However, reservoirs formed by different sedimentary environments tectonic movements generally have characteristics complex diverse strong heterogeneity, which brings great difficulty to reservoir lithology. This paper proposes an automatic technology for based on GWO-SVM algorithm model. Th...

Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in gene profiling and detecting credit card fraud). This paper discusses the factors behind ...

Recently, Support Vector Machine (SVM) has become a very dynamic and popular topic in the Neural Network community for its abilities to perform classification, estimation, and regression. One of the major tasks in the SVM algorithm is to locate the points, or rather support vectors, based on which we construct the discriminant boundary in classification task. In the process of studying the meth...