Off the Mainstream: Advances in Neural Networks and Machine Learning for Pattern Recognition

This Special Issue (SI) originates from an event we organized in Ulm, Germany, in September 2016, namely the seventh IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR2016) [18], sponsored by the International Association for Pattern Recognition (IAPR) and managed by its Technical Committee 3 on Neural Networks and Computational IntelligenceFootnote1 (TC3). In the era of deep learning, ANNPR2016 aimed to create a common ground for researchers active specifically in applications to pattern recognition tasks of neural networks and other machine learning approaches. After the success of the workshop, and in the light of the exquisite scientific contribution of several presentations given therein, it came to our minds the idea of proposing a special issue of a relevant journal, to be based on extended versions of selected papers from ANNPR2016. The Editorial Board of Neural Processing Letters accepted the proposal, encouraging us to proceed with the initiative. At that point, we decided to extend the scope of the SI to an even broader audience by means of an open call for papers on the topic of “off-the-mainstream” approaches to pattern recognition by means of neural nets and learning paradigms. The call received an enthusiastic response by the community. Eventually, this SI is the result of a selection of the best peer-reviewed submissions of both stems.