Linear programming (LP) decoding approximates maximum-likelihood (ML) decoding of a linear block code by relaxing the equivalent ML integer programming (IP) problem into a more easily solved LP ...

The demand for data-driven professionals in healthcare and clinical research continues to grow, making statistical programming a highly rewarding and future-proof role.

We consider the problem of designing output feedback controllers that use measurements from a set of landmarks to navigate through a cell-decomposable environment using duality, control Lyapunov ...

The paper introduces the PILOT learning algorithm for constructing linear model trees, enhancing decision tree interpretability and performance. It uses a standard regression model with centered ...

Select the best ML algorithm by considering problem type, data characteristics, and performance metrics for effective decision-making.

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

Not all use cases are good candidates for machine learning. In this column we look at cases where AI/ML may be appropriate and when building a traditional algorithm to solve a problem is a better ...

Machine learning (ML) — technology that learns from experience (data) to predict the behavior of each individual — is well known for improving the bottom line by running major operations more ...