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The Third International Conference on Data Engineering was held in Los Angeles, California on February 2 to 6, 1987. The conference was designed as an international forum for bringing together researchers, developers, managers, strategic planners and other users with an interest in the research, design and development of data engineering methodologies, strategies and systems. Its scope includes computer science, artificial intelligence, electrical engineering, and computer engineering. It featured papers from all major areas of data engineering, including database design and modeling, performance evaluation, algorithms, integrity, security, fault tolerance, query language, artificial intelligence approaches, knowledge bases, database machines, distributed databases and data engineering applications.

The generalization error bounds found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. These bounds are intended for the entire input space. However, support vector machine (SVM), radial basis function neural network (RBFNN), and multilayer perceptron neural network (MLPNN) are local learning machines for solving problems and treat unseen samples near the training samples to be more important. In this paper, we propose a localized generalization error model which bounds from above the generalization error within a neighborhood of the training samples using stochastic sensitivity measure. It is then used to develop an architecture selection technique for a classifier with maximal coverage of unseen samples by specifying a generalization error threshold. Experiments using 17 University of California at Irvine (UCI) data sets show that, in comparison with cross validation (CV), sequential learning, and two other ad hoc methods, our technique consistently yields the best testing classification accuracy with fewer hidden neurons and less training time. [ABSTRACT FROM AUTHOR]

The Irvine School refers to an intellectual perspective on information technology in complex organizational settings that emerged at the University of California in Irvine, California (UCI), over the last three decades of the 20th century. In many ways, the rise of the Irvine School was synonymous with the rise of researcher Rob Kling's influence on the international community of scholars who would eventually form what Kling called social informatics. This article reflects on Kling's role in the creation of the Irvine School. Kenneth Kraemer, an architect and city planner by training, had joined the UCI Graduate School of Administration in 1967. He was one of the first scholars to begin careful empirical study of the effect of computerization in government agencies, and soon started a research program on information technology and local government at UCI's Public Policy Research Organization. In the early 1970s, researchers Jim Danziger and Rob Kling joined him in this endeavor. Danziger had been trained in political science and public administration. Kling had been trained in electrical engineering and computer science, and had done important work at the intersection of fuzzy logic and planning systems in the area of symbolic artificial intelligence.