This empirical study was inspired by Caruana and Niculescu-Mizil's study to evaluate supervised learning algorithms. Random forests, logisitc regression, and K-nearest neighbors were used for the ...

Semi-supervised learning (SSL) is a machine learning approach ... data to improve the learning accuracy of classification algorithms. This method is particularly useful in scenarios where ...

With many supervised learning algorithms available, it has always peaked interest in which methods will be the most efficient and accurate. Using the UCI repository and the scikit-learn libraries, the ...

Abstract: The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only ...

Abstract: We discuss how a large class of regularization methods, collectively known as spectral regularization and originally designed for solving ill-posed inverse problems, gives rise to ...

Conceptually, Semi-Supervised Learning (SSL) can be positioned at midway between Unsupervised Learning (UL), where no labels are provided and algorithms deconstruct patterns from unlabeled data e. g.