Al-Shehari, T. and Alsowail, R.A. (2021) An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques. Entropy, 23, Article 1258.

Semantic segmentation using LiDAR is a fundamental aspect in perception for autonomous driving. Conventional training methodologies commonly employ one-hot encoding for labels, followed by a softmax ...

One hot encoding can be defined as the essential process of converting the categorical data variables to be provided to machine and deep learning algorithms which in turn improve predictions as well ...

Machine Learning models don't support such data natively. Fortunately, with **one-hot encoding**, we can ensure that we can _still_ use these features - simply by converting them into numeric vector ...

When you are training a Supervised Machine Learning model, you are effectively feeding forward data through the model, comparing the predictions, and improving the model internals - iteratively. These ...

For example, when using the Keras library, you can encode a binary dependent variable using zero-one encoding in conjunction with logistic sigmoid activation, or you can use one-hot encoding in ...