Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …

Learning effective feature representations and similarity measures are crucial to the retrieval
performance of a content-based image retrieval (CBIR) system. Despite extensive research …

The explosive growth in Big Data has attracted much attention in designing efficient indexing
and search methods recently. In many critical applications such as large-scale search and …

The metric learning problem is concerned with learning a distance function tuned to a
particular task, and has been shown to be useful when used in conjunction with nearest …

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose
distances to a query item are the smallest from a large database. Various methods have …

Recently, the AI/ML research community has indicated an urgent need to establish
Responsible AI (RAI) values and practices as part of the AI/ML lifecycle. Several …

Over the past decades, medical image analytics was greatly facilitated by the explosion of
digital imaging techniques, where huge amounts of medical images were produced with …

Learning a measure of similarity between pairs of objects is an important generic problem in
machine learning. It is particularly useful in large scale applications like searching for an …

Abstract The k-Nearest Neighbor (k NN) algorithm is widely used in the supervised learning
field and, particularly, in search and classification tasks, owing to its simplicity, competitive …