The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …

This survey article considers methods and algorithms for fast estimation of data
distance/similarity measures from formed real-valued vectors of small dimension. The …

Granular Computing is a powerful information processing paradigm, particularly useful for
the synthesis of pattern recognition systems in structured domains (eg, graphs or …

In this paper, we address a new scheme for symbol retrieval based on bag-of-relations
(BoRs) which are computed between extracted visual primitives (eg circle and corner). Our …

Graphs have been widely used for complex data representation in many real applications,
such as social network, bioinformatics, and computer vision. Therefore, graph similarity join …

In this paper, we propose two novel local-global nested graph kernels, namely the nested
aligned kernel and the nested reproducing kernel, drawing on depth-based complexity …

In this paper we report on a new structural pattern recognition approach for in silico
prediction of chemical activity. It is based on grammatical inference on strings representing …

Graphs have gained a lot of attention in the pattern recognition community thanks to their
ability to encode both topological and semantic information. Despite their invaluable …

Due to the high availability and applicability, handwritten signatures are an eminent
biometric authentication measure in our life. To mitigate the risk of a potential misuse …

Network data is ubiquitous, such as telecommunication, transport systems, online social
networks, protein-protein interactions, etc. Since the huge scale and the complexity of …