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Dynamic Learning Networks: Models and Cases in Action represents an attempt to provide a network perspective of organizational learning to drive dynamic competition through extended firm learning processes. This edited volume, contributed by worldwide experts in the field, provides academics and company managers with an extended view of organizational learning networks from real cases and different perspectives. Dynamic Learning Networks: Models and Cases in Action is based on the workshop, Managing Uncertainty and Competition through Dynamic Learning Networks. It was organized by the E-Business Management Section of Scuola Superiore ISUFI – University of Salento (Italy) – and held in Ostuni (Italy) in July 2008. Dynamic Learning Networks: Models and Cases in Action is designed for a professional audience, composed of researchers and practitioners working in corporate learning. This volume is also suitable for advanced-level students in computer science.
From creating Web sites to working with snippets and library items, this step-by-step guide gets readers using Dreamweaver CS3 like a pro. The book comes with a companion DVD with video tutorials and all of the files needed for the lessons, including final, completed Dreamweaver files. A free Instructors Guide is available online.
The organization of interfirm networks, such as alliances, cooperatives, franchise and retail chains, has become an important research topic in the field of economics, marketing, strategic management, and organization theory. This book contributes to the literature on formal and informal inter-organizational governance by providing new insights on contract design, ownership, evolution of cooperation, role of social capital and performance in franchising networks; includes topics of loyalty, reputation and organizational form as well as performance of cooperatives, and discusses the relationship between formal and relational governance in alliances, governance structures of innovation activities, dynamics of interfirm conflicts, and network externalities and alliance formation.
Die sechs englischsprachigen Beiträge dokumentieren die Konzepte der von der EU im Rahmen von Prevalet geförderten internationalen Zusammenarbeit zwischen regionalen Regierungen im europäischen Raum sowie dem daraus resultierenden Aufbau eines Systems zur Unterstützung und Beschleunigung des Lern- und Innovationstransfers zwischen den europäischen Regionen. Das in diesem Kontext entwickelte, web-basierte Support-Netzwerk Soft Open Method of Coordination (SMOC) soll die Kooperation zwischen regionalen Regierungen in Weiterbildungsfragen vereinfachen und steht allen an Weiterbildung interessierten Institutionen als Plattform für Information und Austausch zur Verfügung. Der ergänzende Band "Tools for Policy Learning and Policy Transfer" (978-3-7639-3580-2) enthält das empirische Material, die methodische Durchführung und die Präsentation des entwickelten Support-Systems.
Neuronale Netze haben sich in vielen Bereichen der Informatik und künstlichen Intelligenz, der Robotik, Prozeßsteuerung und Entscheidungsfindung bewährt. Um solche Netze für immer komplexere Aufgaben entwickeln zu können, benötigen Sie solide Kenntnisse der Theorie statischer und dynamischer neuronaler Netze. Aneignen können Sie sie sich mit diesem Lehrbuch! Alle theoretischen Konzepte sind in anschaulicher Weise mit praktischen Anwendungen verknüpft. Am Ende jedes Kapitels können Sie Ihren Wissensstand anhand von Übungsaufgaben überprüfen.
The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.
A "Learning Network" is a community of people who help each other to better understand and handle certain events and concepts in work or life. As a result – and sometimes also as an aim – participating in learning networks stimulates personal development, a better understanding of concepts and events, career development, and employability. "Learning Network Services" are Web services that are designed to facilitate the creation of distributed Learning Networks and to support the participants with various functions for knowledge exchange, social interaction, assessment and competence development in an effective way. The book presents state-of-the-art insights into the field of Learning Networks and Web-based services which can facilitate all kinds of processes within these networks.
This account of development in educational research is intended as a guide to possible research areas, both fundamental and policy-related, for students in colleges and higher education institutions, and should also be of interest to those engaged in curriculum planning and administration.
This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
Connectionist Models contains the proceedings of the 1990 Connectionist Models Summer School held at the University of California at San Diego. The summer school provided a forum for students and faculty to assess the state of the art with regards to connectionist modeling. Topics covered range from theoretical analysis of networks to empirical investigations of learning algorithms; speech and image processing; cognitive psychology; computational neuroscience; and VLSI design. Comprised of 40 chapters, this book begins with an introduction to mean field, Boltzmann, and Hopfield networks, focusing on deterministic Boltzmann learning in networks with asymmetric connectivity; contrastive Hebbian learning in the continuous Hopfield model; and energy minimization and the satisfiability of propositional logic. Mean field networks that learn to discriminate temporally distorted strings are described. The next sections are devoted to reinforcement learning and genetic learning, along with temporal processing and modularity. Cognitive modeling and symbol processing as well as VLSI implementation are also discussed. This monograph will be of interest to both students and academicians concerned with connectionist modeling.