Welcome to our book review site www.go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Machine Learning
Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based appr...
Multimedia Techniques for Device and Ambient Intelligence
Ambient Intelligence is a vision of the future where the world will be surrounded by electronic environments sensitive and responsive to people, wherein devices work in concert to support people in carrying out their everyday life activities, in an easy and natural way. This edited volume is based on the workshop Multimedia Techniques for Ambient Intelligence (MTDAI08), held in Mogliano Veneto, Italy in March 2008. Contributed by world renowned leaders in the field from academia and industry, this volume is dedicated to research on technologies used to improve the intelligence capability of multimedia devices for imaging, image processing and computer vision. Focuses on recent developments in digital signal processing, including evolutions in audiovisual signal processing, analysis, coding and authentication, and retrieval techniques. Designed for researchers and professionals, this book is also suitable for advanced-level students in computer science and electrical engineering.
Artificial Neural Networks in Pattern Recognition
This book constitutes the refereed proceedings of the Second IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2006, held in Ulm, Germany in August/September 2006. The 26 revised papers presented were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on unsupervised learning, semi-supervised learning, supervised learning, support vector learning, multiple classifier systems, visual object recognition, and data mining in bioinformatics.
Neural Nets
This book constitutes the thoroughly refereed postproceedings of the 14th Italian Workshop on Neural Networks, WIRN VIETRI 2003, held in Vietri sul Mare, Italy in June 2003. The 41 revised papers presented were carefully reviewed and improved during two rounds of selection and refereeing. The papers are organized in topical sections on models for neural computation; architectures and algorithms; image and signal processing; applications; bioinformatics and statistics; and formats of knowledge: words, images, and narratives.
Structural, Syntactic, and Statistical Pattern Recognition
This is the proceedings of the 11th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2006 and the 6th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2006, held in Hong Kong, August 2006 alongside the Conference on Pattern Recognition, ICPR 2006. 38 revised full papers and 61 revised poster papers are included, together with 4 invited papers covering image analysis, character recognition, bayesian networks, graph-based methods and more.
Advances in Smart Healthcare Paradigms and Applications
This book is dedicated to showcase research and innovation in smart healthcare systems and technologies led by women scientists, researchers, and practitioners. With the advent of artificial intelligence (AI) and related technologies, the healthcare sector has undergone tremendous changes in practice and management in recent years. On par to men, women have made significant contributions to tackle a variety of healthcare problems, creating smarter paradigms to provide effective and efficient solutions for patients and stakeholders. The book presents a small collection of contributions by outstanding women in STEM (Science, Technology, Engineering and Mathematics) education, focusing on the healthcare domain. The selected articles allow readers to comprehend current advances in AI and other methods for undertaking healthcare challenges. It is envisaged that the inspiring work by prominent women scientists, researchers, and practitioners reported in this book offers a beacon to propel women in pursuing STEM education and advancing the healthcare sector for the benefits of humankind.
Handbook of Artificial Intelligence in Healthcare
This handbook on Artificial Intelligence (AI) in healthcare consists of two volumes. The first volume is dedicated to advances and applications of AI methodologies in specific healthcare problems, while the second volume is concerned with general practicality issues and challenges and future prospects in the healthcare context. The advent of digital and computing technologies has created a surge in the development of AI methodologies and their penetration to a variety of activities in our daily lives in recent years. Indeed, researchers and practitioners have designed and developed a variety of AI-based systems to help advance health and well-being of humans. In this first volume, we present a number of latest studies in AI-based tools and techniques from two broad categories, viz., medical signal, image, and video processing as well as healthcare information and data analytics in Part 1 and Part 2, respectively. These selected studies offer readers practical knowledge and understanding pertaining to the recent advances and applications of AI in the healthcare sector.
Optimization Techniques
Optimization Techniques is a unique reference source to a diverse array of methods for achieving optimization, and includes both systems structures and computational methods. The text devotes broad coverage toa unified view of optimal learning, orthogonal transformation techniques, sequential constructive techniques, fast back propagation algorithms, techniques for neural networks with nonstationary or dynamic outputs, applications to constraint satisfaction,optimization issues and techniques for unsupervised learning neural networks, optimum Cerebellar Model of Articulation Controller systems, a new statistical theory of optimum neural learning, and the role of the Radial Basis Function in ...
