The paper categorizes and reviews the state-of-the-art approaches to the partially
supervised learning (PSL) task. Special emphasis is put on the fields of pattern recognition …

Clustering is a well-known unsupervised machine learning approach capable of
automatically grouping discrete sets of instances with similar characteristics. Constrained …

Finding an appropriate distance metric that accurately reflects the (dis) similarity between
examples is a key to the success of k-means clustering. While it is not always an easy task to …

Vehicle routing problems (VRPs) are essential in logistics. In the literature, many exact and
heuristic optimization algorithms have been proposed to solve the VRPs. These traditional …

This paper presents a distance metric learning method for k-nearest neighbors regression.
We define the constraints based on triplets, which are built from the neighborhood of each …

The fundamental goal of self-supervised learning (SSL) is to produce useful representations
of data without access to any labels for classifying the data. Modern methods in SSL, which …

We study the problem of learning from aggregate observations where supervision signals
are given to sets of instances instead of individual instances, while the goal is still to predict …

Distance metric is of considerable importance in varieties of machine learning and pattern
recognition applications. Neighborhood component analysis (NCA), one of the most …

Several studies show that background knowledge of a domain can improve the results of
clustering algorithms. In this paper, we illustrate how to use the background knowledge of …

Affinity propagation (AP) is a classic clustering algorithm. To improve the classical AP
algorithms, we propose a clustering algorithm namely, adaptive spectral affinity propagation …