Titlebook: Knowledge Management and Acquisition for Smart Systems and Services; 13th Pacific Rim Kno Yang Sok Kim,Byeong Ho Kang,Deborah Richards Conf

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Shahril Irwan Sulaiman,Nur Zahidah Zainol,Zulkifli Othman,Hedzlin Zainuddinnt/practitioner. The software codes are put in a CD for convenience of the students/practitoner of geostatistics. A few additions have been made in the bibliography making it more exhaustive. This contains references to the concepts and methods presented, in-depth treatment of related topics and possible extensions. My grat978-1-4020-9380-7

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,-norm Multiple Kernel Learning with Diversity of Classes handle binary problems. However, multi-class problems appear in many real applications. In this paper, we propose a novel model, ..-norm multiple kernel learning with diversity of classes (LMKLDC), for the multi-class multiple kernel learning problem. LMKL-DC focuses on diversity of classes and aim

敲詐

Design of Technology Value Analysis System Based on Patent Big Datar a company to survive in the competition. Recently, a few methodologies and systems analyzing the trend of future technology are being studied. Especially, there are some studies, which explore future technology by huge patent documents. However, the existing systems for it have limitation issues i

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Problems Detected by a Ripple-Down Rules Based Medication Review Decision Support System: Are They Rcial implementation of this system (MRM) was evaluated to determine how many additional DRPs would be identified by the reviewing pharmacist when supported by MRM, and whether these additional DRPs were clinically relevant. The DRPs identified by pharmacists were compared against those found by MRM

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Linked Production Rules: Controlling Inference with Knowledgee can be separated from the domain knowledge being reasoned about. We suggest that the knowledge acquisition and maintenance problems that arise, might result from too great a separation of knowledge and inference. We propose Linked Production Rules, where each rule evaluated directs the next step o

BLA

Using a Domain Expert in Semi-supervised Learning a domain. Since the labeled data should be representative of the range of unlabeled data available, the aim of this research is to identify which data should be labeled. An approach has been developed where a domain expert starts to label unlabeled data and also writes rules to classify such data.