A note on core research issues for statistical pattern recognition
چکیده
Is statistical pattern recognition a collection of topics and techniques borrowed from other fields? Or has it an identity of its own, generating specific research questions, producing its own theoretical framework? This question arises occasionally between participants of the workshops and conferences focusing on the statistically oriented procedures and applications in pattern recognition. Although many pattern recognition scientists seem pretty sure about the cultural identity of their research field, the authors think that the recent developments of closely related disciplines (e.g., machine learning and neural networks) and the increasing number of research issues that pattern recognition shares with such disciplines makes the answer to the above question more difficult than in past times. Therefore, this paper aims to stimulate discussion in the pattern recognition community on the structural differences between statistical pattern recognition and closely related disciplines in order to clarify the present cultural identity and the core research issues. According to computer science encyclopaedia and some famous textbooks, pattern recognition (PR) can be defined as the discipline that studies theories and methods for designing machines that are able to recognise patterns in noisy data (Srihari and Govindaraju, 1993; Duda et al., 2001; Fukunaga, 1990; Devijver and Kittler, 1982). Although this is only one of the possible definitions for PR, it points out well the ‘‘engineering’’ nature of this scientific discipline, because it says that the final goal of PR is the design of ‘‘machines’’. In this engineering perspective on PR, it is quite obvious that PR theory has multi-disciplinary roots, because engineering disciplines aim to bridge the gap between real-world applications and the so-called pure disciplines (mathematics, statistics, physics, etc.). It is worth noting that this multi-disciplinary character of the theoretical foundations of PR was already evident in the seminal book by Watanabe (1985). Watanabe pointed out that pattern recognition can be regarded as a statistical decisionmaking task, as a structure analysis problem, as an induction process, as a perception task, etc., thereby acknowledging the multiple cultural roots of PR. With regard to the strong relations between PR and artificial intelligence, Tveter recently stated that many artificial intelligence methods can be Pattern Recognition Letters 23 (2002) 493–499