Explore essential techniques for encoding categorical data in machine learning, including one-hot, label, and binary encoding methods. Skip to main content LinkedIn Articles ...

Data encoding can be beneficial for data visualization, however, it can also bring some challenges or pitfalls such as over-encoding, under-encoding, and misleading-encoding.

This technique addresses issues such as generalization loss, resource intensity, and data redundancy that often affect compressed models. EnCoDe's utility is validated using SVHN and CIFAR-10 datasets ...

With the needs of science and business, data sharing and re-use has become an intensive activity for various areas. In many cases, governance imposes rules concerning data use, but there is no ...

The ENCODE data model could be useful in other fields: for example, astronomy and Earth science are in the process of organizing their reams of data (M. J. Raddick and A. S. Szalay Science 329, ...

We present ENCODE, an innovative ensemble-based Contextual Bandit (CB) model, engineered explicitly for dynamic pricing in large-scale e-commerce for kid’s clothing. ENCODE uniquely addresses the ...