Artificial Neural Networks and Statistical Pattern Recognition: Old and New Connections

North Holland, 1991 8vo (22.5 cm), XIV, 271 pp. Laminated wrappers. The book explores the intriguing interplay between two prominent fields in the realm of machine learning and data analysis. This thought-provoking book delves into the historical development and the modern-day synergies between artificial neural networks (ANNs) and statistical pattern recognition (SPR), shedding light on their shared roots and divergent paths. By examining the historical context, readers gain a comprehensive understanding of how ANNs and SPR have evolved independently over time. The book traces their origins, from the early perceptron models and foundational statistical techniques to the emergence of deep learning and advanced probabilistic models. Through this historical lens, it becomes evident that ANNs and SPR have followed distinct trajectories, with ANNs prioritizing flexible, connectionist approaches, and SPR emphasizing probabilistic modeling and statistical inference. However, the book goes beyond mere historical analysis and illuminates the contemporary convergence of ANNs and SPR. It explores how recent developments in both fields have led to a remarkable convergence, creating new avenues for collaboration and synergy. The advent of deep learning, which combines ANN architectures with large-scale data and computational power, has revolutionized both ANNs and SPR, unlocking new possibilities for pattern recognition, data analysis, and decision-making.