This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.
Breast cancer is the most frequent cancer of women in the western hemisphere. This book presents a new imaging modality of the breast which improves the possibilities of mammography at a very high level: Cancers can be detected at a very early stage y MRM. The huge number ofbreast biopsies can be reduced dramatically. Even tiny breast cancers (e.g.3mm) can be detected. The prognosis for women with breast cancer will improve due to earlier detection.
Magnetic resonance imaging (MRI) is a new and still rapidly developing imaging technique which requires a new approach to image interpreta tion. Radiologists are compelled to translate their experience accumulat ed from X-ray techniques into the language of MRI, and likewise stu dents of radiology and interested clinicians need special training in both languages. Out of this necessity emerged the concept of this book as a manual on the application and evaluation of proton MRI for the radiolo gist and as a guide for the referring physician who wants to learn about the diagnostic value of MRI in specific conditions. After a short section on the basic principles of MRI, the contrast mechanisms of present-day imaging techniques, knowledge of which is essential for the analysis of relaxation times, are described in greater de tail. This is followed by a demonstration of functional neuroanatomy us ing three-dimensional view of MR images and a synopsis of frequent neurological symptoms and their topographic correlations, which will fa cilitate examination strategy with respect to both accurate diagnosis and economy.
This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.
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