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" ... Presents a process for classifying shapes in digital images using a radial feature token (RFT) and classical statistical methods to learn shapes from training examples and then classify similar shapes in test images."--Introduction
The ALISA Component Module: General shape classification for digital radiographic images.
Many organizations are now realizing that their competitive edge lies mostly in the brainpower-the intellectual capital-of their employees and management. To stay ahead of the pack, companies must leverage their knowledge, internally and externally. But it is not enough to develop lessons-learned databases. Experts now believe the current savior of organizations is knowledge management-the conceptualization, review, consolidation, and action phases of creating, securing, combining, coordinating, and retrieving knowledge-in short, the process of creating value from an organization's intangible assets. Jay Liebowitz, one of the leading knowledge management and expert systems authorities in the world, brings together over thirty articles contributed by the top researchers and practitioners to produce what seems destined to become the key reference for this emerging field. With it you will find: How to create a knowledge-sharing environment How senior executives can show tangible benefits using methods that value the intellectual capital-especially the "human capital" within the organization How knowledge management is not the same as information management How senior management commitment and involvement are essential to the success of a knowledge management system
The October 2000 workshop focused on image analysis and processing algorithms and applications that benefit from or are required by new digital image sources. The 39 papers are divided into sessions on satellite and aerial image classification, image storage and retrieval, image compression and codi
Proceedings of SPIE present the original research papers presented at SPIE conferences and other high-quality conferences in the broad-ranging fields of optics and photonics. These books provide prompt access to the latest innovations in research and technology in their respective fields. Proceedings of SPIE are among the most cited references in patent literature.
This book constitutes the refereed proceedings of the 4th Mexican International Conference on Artificial Intelligence, MICAI 2005, held in Monterrey, Mexico, in November 2005. The 120 revised full papers presented were carefully reviewed and selected from 423 submissions. The papers are organized in topical sections on knowledge representation and management, logic and constraint programming, uncertainty reasoning, multiagent systems and distributed AI, computer vision and pattern recognition, machine learning and data mining, evolutionary computation and genetic algorithms, neural networks, natural language processing, intelligent interfaces and speech processing, bioinformatics and medical applications, robotics, modeling and intelligent control, and intelligent tutoring systems.
In this extraordinary new book, a pioneer in the research on Collective Learning Systems (an adaptive learning paradigm for artificial intelligence) describes the processes and mechanisms of human and artificial cognition, defines a fundamental building block for assembling large-scale adaptive systems (the learning cell) and proposes a design for the ultimate machine: a hierarchical network of 100 million learning cells that could exhibit the full range of cognitive capabilities of the human cerebral cortex.The author demonstrates that using the classical “expert system” approach to create such a vast knowledge base would require thousands of years to program all the necessary rules. He then explains how an adaptive Collective Learning System could achieve this goal in a matter of 20 years, much as humans do. Based on natural anatomical and behavioral precedents, Collective Learning enables a machine to learn the appropriate rules through trial-and-error interaction with the real world.In the course of explaining the principles of Collective Learning and his design for the ultimate machine, the author introduces a new theory of games for modelling the processes of the universe and discusses the philosophical issues raised by the prospect of creating machines that exhibit human-like intelligence. In addition to a number of small-scale software illustrations of Collective Learning, the final chapter presents the remarkable results of a large-scale research project directed by the author: a hardware and software simulation of the sub-symbolic image-processing functions of the primary visual cortex of the brain.To make the content palatable to a wide variety of readers, the book is written in a conversational style and laced with humor.Lengthy mathematical derivations and proofs have been omitted or abbreviated. Bibliographical references to scholarly journal papers and books are included to guide theoreticians to the attendant formalisms.