A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...

The Sports Analytics Research Group employs quantitative analysis to give teams the hard numbers they need to perform better ...

As humans, our eyes take in two-dimensional images that our brains convert to three-dimensional experiences. This ability enables us to be aware of our position in space, judge distances, possess ...

Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

Abstract: The prediction algorithm is one of the most important factors in the quality of wind-power prediction. In this paper, based on the principles of wavelet transform and support vector machines ...

Two-sample testing examines whether two probability distributions on some feature space differ based on random samples. It is fundamental in statistics and machine learning, especially when feature ...

Considering the strong non-linear time-varying behavior of dam deformation, a novel prediction model, called Levy flight-based grey wolf optimizer optimized support vector regression (LGWO-SVR), is ...

Abstract: This study investigates the sample value imbalance problem of process monitoring. A fault detection approach based on variable selection and support vector data description (SVDD) is ...

Operational faults in centrifugal chillers will lead to high energy consumption, poor indoor thermal comfort, and low operational safety, and thus it is of significance to detect and diagnose the ...

In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among the most widely used machine learning methods for the identification of new active compounds. In ...