Artificial Neural Networks Pattern (170 results)

Condition: Good. Good condition. Volume TT04. A copy that has been read but remains intact. May contain markings such as bookplates, stamps, limited notes and highlighting, or a few light stains.

Condition: New. 271 pp., Hardcover, BRAND NEW, still in shrink wrap. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

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paperback. Condition: Fine.

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Soft cover. Condition: As New. 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.

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paperback. Condition: New. New. book.

Softcover. Condition: New. Dust Jacket Condition: no dj. First. Novel Approaches to Training Artificial Neural Networks for Pattern Recognition presents a cutting-edge examination of the latest advancements in training artificial neural networks for pattern recognition tasks. This book explores the application of innovative algorithms, architectures, and techniques to enhance the performance and efficiency of neural networks in recognizing complex patterns. By examining the theoretical foundations and practical implementations of these novel approaches, the book provides a comprehensive understanding of the potential applications of neural networks in fields such as computer vision, natural language processing, and predictive analytics. From the development of novel activation functions to the integration of transfer learning and ensemble methods, this book offers a detailed exploration of the latest trends and advancements in neural network training. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condition: New. Dust Jacket Condition: no dj. First. In the realm of artificial intelligence, the quest for improved pattern recognition has led to the development of novel approaches to training neural networks. This book explores the cutting-edge techniques that have revolutionized the field, from the application of transfer learning to the utilization of meta-heuristics. By examining the intricacies of neural network architecture and the role of hyperparameter tuning, readers will gain a deeper understanding of the complex interplay between model design and performance. The book also delves into the realm of deep learning, where convolutional neural networks and recurrent neural networks are employed to tackle a wide range of pattern recognition tasks. With its focus on the latest advancements and the most effective strategies, this book is an essential resource for researchers and practitioners seeking to push the boundaries of artificial intelligence. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condition: New. Dust Jacket Condition: no dj. First. Memory and Pattern Formation in Artificial Neural Networks examines the mechanisms by which artificial systems emulate human memory processes and pattern recognition. The book details the architecture of neural networks designed for learning and memory storage, highlighting how these models can replicate complex cognitive functions. It discusses the algorithms that enable pattern formation and the implications for artificial intelligence development. This comprehensive analysis bridges neuroscience and computer science, illustrating how artificial neural networks can serve as models for understanding biological memory systems. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condition: New. Dust Jacket Condition: no dj. First. This book offers a fascinating exploration of the parallels between human memory and pattern formation in artificial neural networks. By examining the ways in which neural networks learn and adapt, the author reveals the intricate mechanisms underlying memory formation and retrieval. Through a series of theoretical analyses and computational simulations, 'Memory and Pattern Formation in Artificial Neural Networks' provides a richly detailed understanding of the complex relationships between memory, cognition, and computation. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The use of hematological analyzers has become routine in clinical practice, but the sheer volume of data produced by these devices often makes manual inspection of all the results an unwieldy task. For this reason, automated pattern analysis through the use of machine learning has been used in these types of situations, to save time and to provide invaluable aid to medical professionals in this area of diagnostic medicine. Toward this end, Artificial Neural Networks (ANNs) are often relied upon in the field of machine learning, because of their ability to distill representative feature components from large amounts of input data. This paper details an approach in which the scatterplots of cells that were produced by a hematological device were used as inputs. The data were separated into two classes, one containing clinically Normal samples, and the second containing abnormal samples that contained Variant Lymphocytes. Statistical features were extracted from these data using Principal Component Analysis (PCA) and then a Perceptron ANN was employed to differentiate between the two classes of data. The accuracy of pattern classification using this method was then discussed.

Paperback. Condition: Brand New. 84 pages. 8.66x5.91x0.19 inches. In Stock. This item is printed on demand.