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Active noise canceling with on-chip learning based on analog neuro-chips with on-chip learning capability
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Back-Propagation Algorithm for Refinement of Multilayer Dielectric Optical Interference Filter
No full textDesign of Optical Thin-Film Systems using Error Backpropagating Learning Algorithm of Neural Network
No full textExpandable Analog Neural Network Chip Set With on-Chip Learning by Error Back-Propagation and Hebbian Learning Rules
No full textOn-Line Handwritten Character Recognition by a Hybrid Method Based on Neural Networks and Pattern Matching
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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