Pattern recognition: Evolution, mining and big data
Document Type
Book Chapter
Publication Title
Pattern Recognition and Big Data
Abstract
This chapter traces the evolution of pattern recognition (PR) over the years, from its humble beginnings as an extension of statistical discrim- inant analysis, to the multidisciplinary approach that it has become now, on account of the continuous import of ideas from various scien- tific disciplines. It begins with an introduction to the discipline of PR, explaining the basic underlying concepts, different tasks involved, some conventional classification techniques and the subsequent development of various modern methodologies. The evolution has been nurtured and aided by the likes of statistical decision theory, the theory of formal lan- guages (which led to the syntactic or structural approach), followed by the theories of fuzzy sets, artificial neural networks, genetic algorithms, rough sets, granular computing and support vector machines individually (leading to different modern approaches), and finally, their integration into the theory of soft computing. While tracing the journey of pattern recognition along this complex route, significant aspects are highlighted. The chapter also discusses the significance of data mining, which has drawn the attention of many PR researchers world-wide for the past couple of decades. Finally, the challenging issues of Big Data analysis are addressed along with the relevance of PR and machine learning.
First Page
1
Last Page
36
DOI
10.1142/9789813144552_0001
Publication Date
12-15-2016
Recommended Citation
Pal, Amita and Pal, Sankar K., "Pattern recognition: Evolution, mining and big data" (2016). Book Chapters. 247.
https://digitalcommons.isical.ac.in/book-chapters/247
