Building pattern recognition applications with the SPARE library
This work provides a general-purpose library for researchers and practitioners to develop pattern recognition applications, but it is incremental as it builds on existing algorithms without introducing new methods.
The authors introduced the SPARE C++ library, an open-source tool for building pattern recognition and soft computing systems that can handle diverse data types like labeled graphs and sequences, and demonstrated its utility through application examples in clustering and classification.
This paper presents the SPARE C++ library, an open source software tool conceived to build pattern recognition and soft computing systems. The library follows the requirement of the generality: most of the implemented algorithms are able to process user-defined input data types transparently, such as labeled graphs and sequences of objects, as well as standard numeric vectors. Here we present a high-level picture of the SPARE library characteristics, focusing instead on the specific practical possibility of constructing pattern recognition systems for different input data types. In particular, as a proof of concept, we discuss two application instances involving clustering of real-valued multidimensional sequences and classification of labeled graphs.