Many machine learning algorithms need a similarity measure to carry out their tasks. Usually, standard distances, like euclidean distance, are used to measure this similarity. Distance Metric Learning ...

Welcome! This is a Matlab toolkit for distance metric learning, including the implementation of a number of published machine learning algorithms in this area. The first version of this toolkit has ...

The experiences led Clark to a conclusion that he’s carried forward in his work at SigOpt: Choosing the right thing to measure and getting the right metrics in place are extremely critical to ...

How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric ...

Abstract: In many real-world domains, datasets with imbalanced class distributions occur frequently, which may confuse various machine learning tasks. Among all these tasks, learning classifiers from ...

Machine Learning Basics: Where Bias and Variance Fit in Overfit–Underfit Overfit is a condition that treats noise in training data as a reliable indicator rather than an anomaly.