From the Publisher: Most machine learning research has been concerned with the development of systems that implement one type of inference within a single representational paradigm. Such systems, which can be called m...
ISBN:
(纸本)0792393740
From the Publisher: Most machine learning research has been concerned with the development of systems that implement one type of inference within a single representational paradigm. Such systems, which can be called monostrategy learning systems, include those for empirical induction of decision trees or rules, explanation-based generalization, neural net learning from examples, genetic algorithm-based learning, and others. Monostrategy learning systems can be very effective and useful if the learning problems they are applied to are sufficiently narrowly defined. Many real-world applications, however, pose learning problems that go beyond the capability of monostrategy learning methods. In view of this, recent years have witnessed a growing interest in developing multistrategy systems that integrate two or more inference types and/or paradigms within one learning system. Such multistrategy systems take advantage of the complementarity of different inference types or representational mechanisms. Therefore, they have a potential to be more versatile and more powerful than monostrategy systems. On the other hand, due to their greater complexity, their development is significantly more difficult and represents a new great challenge to the machine learning community. Multistrategy Learning contains contributions characteristic of the current research in this area. It is an edited volume of original research comprising invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 11, Nos. 2/3).
From the Publisher: Vision based mobile robot guidance has proven difficult for classical machine vision methods because of the diversity and real time constraints inherent in the task. This book describes a connectio...
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ISBN:
(纸本)0792393732
From the Publisher: Vision based mobile robot guidance has proven difficult for classical machine vision methods because of the diversity and real time constraints inherent in the task. This book describes a connectionist system called ALVINN (Autonomous Land Vehicle In a Neural Network) that overcomes these difficulties. ALVINN learns to guide mobile robots using the back-propagation training algorithm. Because of its ability to learn from example, ALVINN can adapt to new situations and therefore cope with the diversity of the autonomous navigation task. But real world problems like vision based mobile robot guidance present a different set of challenges for the connectionist paradigm. Among them are: how to develop a general representation from a limited amount of real training data, how to understand the internal representations developed by artificial neural networks, how to estimate the reliability of individual networks, how to combine multiple networks trained for different situations into a single system, and how to combine connectionist perception with symbolic reasoning. Neural Network Perception for Mobile Robot Guidance presents novel solutions to each of these problems. Using these techniques, the ALVINN system can learn to control an autonomous van in under 5 minutes by watching a person drive. Once trained, individual ALVINN networks can drive in a variety of circumstances, including single-lane paved and unpaved roads, and multi-lane lined and unlined roads, at speeds of up to 55 miles per hour. The techniques also are shown to generalize to the task of controlling the precise foot placement of a walking robot.
From the Publisher: Application-Driven Architecture Synthesis describes the state-of-the-art in architectural synthesis for complex real-time processing. In order to deal with the stringent timing requirements and the...
ISBN:
(纸本)0792393554
From the Publisher: Application-Driven Architecture Synthesis describes the state-of-the-art in architectural synthesis for complex real-time processing. In order to deal with the stringent timing requirements and the intricacies of complex real-time signal and data processing, target architecture styles and target application domains have been adopted to make the synthesis approach feasible. These approaches are also heavily application-driven which is illustrated by many realistic demonstrations, used as examples throughout the book. The focus is on domains where application-specific solutions are attractive such as significant parts of audio, telecom, instrumentation, speech, robotics, medical and automotive processing, image and video processing, TV, multi-media, radar, and sonar. Application-Driven Architecture Synthesis is of interest to both academia and to senior design engineers and CAD managers in industry. It provides an excellent overview of what capabilities to expect from future practical design tools and includes an extensive bibliography.
The two volumes of Foundations of Knowledge Acquisition document the recent progress of basic research in knowledge acquisition sponsored by the Office of Naval Research. This volume is subtitled Machine Learning, ...
ISBN:
(纸本)9780792392781
The two volumes of Foundations of Knowledge Acquisition document the recent progress of basic research in knowledge acquisition sponsored by the Office of Naval Research. This volume is subtitled Machine Learning, and there is a companion volume subtitled Cognitive Models of Complex Learning. Funding was provided by a five-year Accelerated Research Initiative (ARI), and made possible significant advances in the scientific understanding of how machines and humans can acquire new knowledge so as to exhibit improved problem-solving behavior. Significant progress in machine learning is reported along a variety of fronts. Chapters in Machine Learning include work in analogical reasoning; induction and discovery; learning and planning; learning by competition, using genetic algorithms; and theoretical limitations. Knowledge acquisition as pursued under the ARI was a coordinated research thrust into both machine learning and human learning. Chapters in Cognitive Modles of Complex Learning, also published by Kluwer Academic Publishers, include summaries of work by cognitive scientists who do computational modeling of human learning. In fact, an accomplishment of research previously sponsored by ONR's Cognitive science Program was insight into the knowledge and skills that distinguish human novices from human experts in various domains; the Cognitive interest in the ARI was then to characterize how the transition from novice to expert actually takes place. These volumes of Foundations of Knowledge Acquisition serve as excellent reference sources by bringing together descriptions of recent and on-going research at the forefront of progress in one of the most challenging arenas of artificial intelligence and cognitive science. In addition, contributing authors comment on exciting future directions for research.
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