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Full-Text Articles in Databases and Information Systems
The Ups Prototype: An Experimental End-User Service Across E-Print Archives, Herbert Van De Sompel, Thomas Krichel, Michael L. Nelson, Patrick Hochstenbach, Victor Lyapunov, Kurt Maly, Mohammad Zubair, Mohamed Kholief, Xiaoming Liu, Heath O'Connell
The Ups Prototype: An Experimental End-User Service Across E-Print Archives, Herbert Van De Sompel, Thomas Krichel, Michael L. Nelson, Patrick Hochstenbach, Victor Lyapunov, Kurt Maly, Mohammad Zubair, Mohamed Kholief, Xiaoming Liu, Heath O'Connell
Computer Science Faculty Publications
A meeting was held in Santa Fe, New Mexico, October 21-22, 1999, to generate discussion and consensus about interoperability of publicly available scholarly information archives. The invitees represented several well known e-print and report archive initiatives, as well as organizations with interests in digital libraries and the transformation of scholarly communication. The central goal of the meeting was to agree on recommendations that would make the creation of end-user services -- such as scientific search engines and linking systems -- for data originating from distributed and dissimilar archives easier. The Universal Preprint Service (UPS) Prototype was developed in preparation for …
Supervised Adaptive Resonance Theory And Rules, Ah-Hwee Tan
Supervised Adaptive Resonance Theory And Rules, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Supervised Adaptive Resonance Theory is a family of neural networks that performs incremental supervised learning of recognition categories (pattern classes) and multidimensional maps of both binary and analog patterns. This chapter highlights that the supervised ART architecture is compatible with IF-THEN rule-based symbolic representation. Specifically, the knowledge learned by a supervised ART system can be readily translated into rules for interpretation. Similarly, a priori domain knowledge in the form of IF-THEN rules can be converted into a supervised ART architecture. Not only does initializing networks with prior knowledge improve predictive accuracy and learning efficiency, the inserted symbolic knowledge can also …