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Smart Green World?
In this book, Steffen Lange and Tilman Santarius investigate how digitalization influences environmental and social sustainability. The information revolution is currently changing the daily lives of billions of people worldwide. At the same time, the current economic model and consumerist lifestyle needs to be radically transformed if society is to overcome the challenges humanity is facing on a finite planet. Can the much-discussed disruption potential of digitalization be harnessed for this purpose? Smart Green World? provides guiding principles for a sustainable digital society and develops numerous hands-on proposals for how digitalization can be shaped to become a driving force for soc...
Learning Theory
This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA, June 2006. The book presents 43 revised full papers together with 2 articles on open problems and 3 invited lectures. The papers cover a wide range of topics including clustering, un- and semi-supervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, and more.
Algorithmic Learning Theory
Annotation This volume contains the papers that were presented at theThird Workshop onAlgorithmic Learning Theory, held in Tokyoin October 1992. In addition to 3invited papers, the volumecontains 19 papers accepted for presentation, selected from29 submitted extended abstracts. The ALT workshops have beenheld annually since 1990 and are organized and sponsored bythe Japanese Society for Artificial Intelligence. The mainobjective of these workshops is to provide an open forum fordiscussions and exchanges of ideasbetween researchers fromvarious backgrounds in this emerging, interdisciplinaryfield of learning theory. The volume is organized into partson learning via query, neural networks, inductive inference, analogical reasoning, and approximate learning.
Algorithmic Learning Theory
This volume presents the proceedings of the Fourth International Workshop on Analogical and Inductive Inference (AII '94) and the Fifth International Workshop on Algorithmic Learning Theory (ALT '94), held jointly at Reinhardsbrunn Castle, Germany in October 1994. (In future the AII and ALT workshops will be amalgamated and held under the single title of Algorithmic Learning Theory.) The book contains revised versions of 45 papers on all current aspects of computational learning theory; in particular, algorithmic learning, machine learning, analogical inference, inductive logic, case-based reasoning, and formal language learning are addressed.
Algorithmic Learning Theory
This book constitutes the refereed proceedings of the 11th International Conference on Algorithmic Learning Theory, ALT 2000, held in Sydney, Australia in December 2000. The 22 revised full papers presented together with three invited papers were carefully reviewed and selected from 39 submissions. The papers are organized in topical sections on statistical learning, inductive logic programming, inductive inference, complexity, neural networks and other paradigms, support vector machines.
Nonmonotonic and Inductive Logic
This proceedings volume contains revised and reviewed papers based on talks presented at the first International Workshop on Nonmonotonic and Inductive Logic held in Karlsruhe, December 1990. The workshop was supported by the Volkswagen-Stiftung, Hannover, and provided a forum for researchers from the two fields to communicate and find areas of cooperation. The papersare organized into sections on: - Nonmonotonicity in logic programs - Axiomatic approach to nonmonotonic reasoning - Inductive inference - Autoepistemic logic - Belief updates The bulk of the papers are devoted to nonmonotonic logic and provide an up-to-date view of the current state of research presented by leading experts in the field. A novelty in the contributions from the area of inductive logic is the analysis of nonmonotonicity in the theory of inductive learning.
Algorithmic Learning Theory
This book constitutes the refereed proceedings of the 16th International Conference on Algorithmic Learning Theory, ALT 2005, held in Singapore in October 2005. The 30 revised full papers presented together with 5 invited papers and an introduction by the editors were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on kernel-based learning, bayesian and statistical models, PAilearning, query-learning, inductive inference, language learning, learning and logic, learning from expert advice, online learning, defensive forecasting, and teaching.
The Future of Responsible Management Education
Business schools have been criticized for several things, such as lacking relevance, a too weak ethics orientation, dated paradigms, or commercialization. Simultaneously, there has been much positive change and accelerated dynamics toward forming future-ready companies and graduates. This book outlines how to better understand and master the digital transformation challenge. It is essential that business school deans, program directors, and faculty members embrace new opportunities to bring the UN-backed Principles of Responsible Management Education (PRME) to life successfully. Part of the Humanism in Business series, this book constitutes a valuable resource for leaders in universities and business schools, as well as individual faculty members aspiring to optimize how they respond to digital transformation. It can also be of use to those studying responsible management education, leadership and business ethics more generally.
Algorithmic Learning Theory
This book constitutes the refereed proceedings of the 14th International Conference on Algorithmic Learning Theory, ALT 2003, held in Sapporo, Japan in October 2003. The 19 revised full papers presented together with 2 invited papers and abstracts of 3 invited talks were carefully reviewed and selected from 37 submissions. The papers are organized in topical sections on inductive inference, learning and information extraction, learning with queries, learning with non-linear optimization, learning from random examples, and online prediction.
Dictionary of Ecological Economics
This comprehensive Dictionary brings together an extensive range of definitive terms in ecological economics. Assembling contributions from distinguished scholars, it provides an intellectual map to this evolving subject ranging from the practical to the philosophical.
