Overview Understanding key machine learning algorithms is crucial for solving real-world data problems effectively.Data scientists should master both supervised ...

In this paper, a novel hybrid approach integrating genetic algorithm (GA) and support vector machines (SVM) is proposed to conduct the key factor exploration tasks in the core competitiveness ...

Medical datasets often present a major challenge for machine learning models: skewness in continuous variables such as age, tumor size, and survival months. This skewness can undermine the assumptions ...

Support vector machines (SVM): SVM is a powerful classification and regression algorithm that performs well in dealing with non-linear relationships.

He’s credited with coming up with the first support vector machine (SVM) algorithm. SVMs are widely used today for machine learning purposes.

Other common machine learning regression algorithms (short of neural networks) include Naive Bayes, Decision Tree, K-Nearest Neighbors, LVQ (Learning Vector Quantization), LARS Lasso, Elastic Net ...

The BO-GBRT model accurately predicts compressive strength in self-compacting concrete with recycled aggregates, improving ...