Abstract: Feature extraction and selection in the presence of nonlinear dependencies among the data is a fundamental challenge in unsupervised learning. We propose using a Gram-Schmidt (GS) type ...

Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania In the face of increasing cyberattacks, ...

Currently, the Python extension only supports “Run Selection/Line in Native Python REPL”. However, there is no built-in command to run the entire file in the Native Python REPL environment, similar to ...

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, ...

Here's what you should know about the new features. Instagram has rolled out a new slate of new features it says will help users better connect with friends, from reposts to updating locations and ...

Abstract: Online streaming feature selection is an effective approach for handling large-scale streaming data in real-world applications. However, many existing online streaming feature selection ...

Feature selection plays a crucial role in statistical learning by helping models focus on the most relevant predictors while reducing complexity and enhancing interpretability. Lasso regression has ...

A set of notebooks that leverage classical ML algorithms and DL neural nets using TF, Keras and Theano to address a series of issues in the field of conservation and biology.