Among the basic cognitive skills of the biological brain in humans and other mammals, a
fundamental one is the ability to recognize inexact patterns in a sequence of objects or …

Pattern recognition in the graphs domain gained a lot of attention in the last two decades,
since graphs are able to describe relationships (edges) between atomic entities (nodes) …

Embedding spaces are one of the mainstream approaches when dealing with structured
data. Granular Computing, in the last decade, emerged as a powerful paradigm for the …

The synthesis of a pattern recognition system usually aims at the optimization of a given
performance index. However, in many real-world scenarios, there exist other desired facets …

Multi-agent systems recently gained a lot of attention for solving machine learning and data
mining problems. Furthermore, their peculiar divide-and-conquer approach is appealing …

Pattern recognition and machine learning problems are often conceived to work on metric
vector spaces, where patterns are described by multi-dimensional feature vectors. However …

Pattern recognition in the graphs domain gained a lot of attention in the last two decades,
since graphs are able to describe relationships (edges) between atomic entities (nodes) …

In many sequence data mining applications, the goal is to find frequent substrings. Some of
these applications like extracting motifs in protein and DNA sequences are looking for …