ML-ISAPR 2018 - 1st Workshop on Machine Learning, Intelligent Systems and Statistical Analysis for Pattern Recognition in Real-life Scenarios (ML-ISAPR 2018)

Pattern recognition is an essential aspect of learning and behaviour analysis for all living things. It may include different natural actions in real-life scenarios, such as recognition of spoken words and languages, discrimination of human traits and fingerprints, visual identification of objects, and capturing the interaction mechanisms among different individuals…
Background and Goals
Pattern recognition is an essential aspect of learning and behaviour analysis for all living things. It may include different natural actions in real-life scenarios, such as recognition of spoken words and languages, discrimination of human traits and fingerprints, visual identification of objects, and capturing the interaction mechanisms among different individuals. Patterns can be found everywhere in multiple contexts, including biology, medicine and healthcare, text and document analysis, image processing and information retrieval. They can assume multiple aspects, including groups of documents characterised by the same language or script, image regions with uniform characteristics, and social communities in complex networks. In recent time, the complex natural phenomena characterising most of the real-life scenarios have required more specialised methods to be introduced for the extraction and recognition of different types of patterns. In particular, machine learning, intelligent systems and statistical analysis are playing a role of prior importance in the extraction, analysis and identification of patterns in different real-life scenarios.
In this context, the main goal of this workshop is presenting the advancement of the state-of-the-art in statistical and data mining tools, as well as the introduction of innovative and intelligent systems for pattern extraction, analysis and recognition aiming to solve real life problems.