Exploring the Maze – Machine Learning for Binary \”Yes/No\” Systems

Machine learning can improve financial risk analysis and medical diagnosis.

Researchers have created a system that allows machines to quickly learn the complex twists and turns of a data system.

Abd-AlRahman Rasheed AlMomani, of Embry-Riddle Aeronautical University, Prescott, Arizona campus, reported that \”Our method could help improve the diagnostics of urinary disease, the imaging and analysis of financial risk.\”

Patterns, a journal published by the Center for Complex Systems Science at Clarkson University, has accepted the research with Jie Sun (the lead researcher) and Erik Bollt (the co-author). The work aims to analyze binary data more efficiently.