High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, ...

The technology is one of the strongest examples yet of how artificial intelligence can be used in a seamless, practical way to improve people’s lives. By Brian X. Chen Brian X. Chen is The Times’s ...

ABSTRACT: Accurately predicting individual responses to antidepressant treatment is a critical step toward achieving personalized psychiatry and minimizing the ...

Department of Chemistry, University of Illinois at Urbana─Champaign, Urbana, Illinois 61801, United States Department of Chemistry, Rice University, Houston, Texas 77005, United States Department of ...

Objective: This study aimed to develop a risk prediction model for post-treatment oligometastasis in nasopharyngeal carcinoma (NPC) by integrating pathomics features and an improved Support vector ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...

1 Department of Electrical and Electronic Engineering, Faculty of Engineering, Sunyani Technical University, Sunyani, Ghana. 2 Department of Electrical and Electronic Engineering, Akenten Appiah-Menka ...

Abstract: Feature selection is an important task in machine learning and binary feature selection is a challenging problem due to the large search space. Swarm intelligent optimization algorithms have ...

Abstract: Feature selection (FS) is a fundamental big data task, improving classification performance by selecting a relevant feature subset to mitigate the `curse of dimensionality'. As the number of ...