We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective
network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most …

The KNN algorithm is one of the most popular data mining algorithms. It has been widely
and successfully applied to data analysis applications across a variety of research topics in …

Learned representations are a central component in modern ML systems, serving a
multitude of downstream tasks. When training such representations, it is often the case that …

The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …

K-nearest neighbor rule (KNN) has been regarded as one of the top 10 methods in the field
of data mining. Due to its simplicity and effectiveness, it has been widely studied and applied …

The k-nearest neighbor (kNN) rule is a classical non-parametric classification algorithm in
pattern recognition, and has been widely used in many fields due to its simplicity …

K nearest neighbor classifier (K-NN) is widely discussed and applied in pattern recognition
and machine learning, however, as a similar lazy classifier using local information for …

The k-nearest neighbor (kNN) classifier is a classical classification algorithm that has been
applied in many fields. However, the performance of the kNN classifier is limited by a simple …

This paper presents new algorithms to identify and eliminate mislabelled, noisy and atypical
training samples for supervised learning and more specifically, for nearest neighbour …

Class imbalance Learning (CIL) is an important machine learning branch. Due to an
imbalanced dataset, the efficiency of the classifiers is impacted. Various under/oversampling …