{"product_id":"spatially-explicit-hyperparameter-optimization-for-neural-networks-9789811653988","title":"Spatially Explicit Hyperparameter Optimization for Neural Networks","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eNeural networks are commonly used machine learning algorithms in GIScience, but there are limited studies on parameter settings and optimizing their performance. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal parameter settings for neural networks, improving computing performance and addressing limitations in current studies. It is written for researchers in GIScience and social science subjects. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 108 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 19 October 2021\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer Verlag, Singapore\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eNeural networks, widely employed in machine learning, have found extensive application in the realm of GIScience to unravel the intricate and nonlinear characteristics of geographic phenomena. While these algorithms, including artificial neural networks (ANNs) and convolutional neural networks (CNNs), have demonstrated their effectiveness in analyzing diverse spatial data, there exists a limited body of research dedicated to exploring the parameter settings of neural networks within the GIScience context. Furthermore, the performance of neural networks often hinges on the judicious selection of parameters for a specific dataset. However, the process of adjusting these parameters can significantly prolong the running time of the models. Consequently, there is a pressing need for an automated approach to address these limitations in current studies.\u003cbr\u003e\u003cbr\u003eIn response to this challenge, this book presents an automated spatially explicit hyperparameter optimization approach aimed at identifying optimal or near-optimal parameter settings for neural networks in the GIScience field. By leveraging advanced optimization techniques, the approach aims to enhance the computing performance at both the model and computing levels. This book is specifically designed to cater to researchers and practitioners in the GIScience domain as well as social science subjects who are interested in leveraging the power of neural networks for spatial data analysis.\u003cbr\u003e\u003cbr\u003eThe book begins by introducing the fundamental concepts and principles of neural networks and their applications in GIScience. It then delves into the challenges associated with parameter optimization, including the need for automated approaches, the impact of parameter settings on model performance, and the trade-off between computational efficiency and model accuracy. The book proceeds to present the proposed automated hyperparameter optimization approach, which encompasses a range of techniques such as genetic algorithm optimization, Bayesian optimization, and gradient descent optimization.\u003cbr\u003e\u003cbr\u003eEach technique is thoroughly discussed, with an emphasis on their theoretical foundations, implementation details, and potential applications in the GIScience field. The book also provides practical examples and case studies to illustrate the effectiveness of the proposed approach in identifying optimal parameter settings for various neural network models. Furthermore, the book discusses the importance of hyperparameter tuning in improving the generalization ability and robustness of neural networks, as well as the potential applications of the optimized parameter settings in other fields such as urban planning, environmental science, and healthcare.\u003cbr\u003e\u003cbr\u003eIn conclusion, this book offers a comprehensive and practical guide to automated spatially explicit hyperparameter optimization for neural networks in GIScience. By presenting advanced optimization techniques and providing practical examples, the book empowers researchers and practitioners to unlock the full potential of neural networks in analyzing spatial data and addressing complex geographic phenomena. Whether you are a GIScience researcher, a data scientist, or a practitioner interested in machine learning, this book is an invaluable resource for advancing your understanding and expertise in this field.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 366g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9789811653988\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2021\u003c\/p\u003e","brand":"Minrui Zheng","offers":[{"title":"Hardback","offer_id":44103298187514,"sku":"9789811653988","price":99.95,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1646111449083_book.jpg?v=1646469001","url":"https:\/\/shulphink.com\/products\/spatially-explicit-hyperparameter-optimization-for-neural-networks-9789811653988","provider":"Shulph Ink","version":"1.0","type":"link"}