The employment of machine learning often requires a large amount of data, including our sensitive and personal information, to train models for various applications. With growing worldwide concerns over data privacy, particularly with the recent introd…
Presents the CIS society calendar of upcoming events and meetings.
Learning to optimize the area under the receiver operating characteristics curve (AUC) performance for imbalanced data has attracted much attention in recent years. Although there have been several methods of AUC optimization, scaling up AUC optimizati…
“For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal b…
Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.
Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.
Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems. However, since deep learning methods operate as black boxes, the uncertainty associated with their predictions is often challenging to quantif…
Presents the front cover for this issue of the publication.
Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation. The concept of evolutionary multitasking (EMT) fills this gap. It unlocks a population&#…
Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.