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    JIIS
    2006
    113views more  JIIS 2006»
    Spatial ordering and encoding for geographic data mining and visualization
    13 years 9 months ago
    Spatial ordering and encoding for geographic data mining and visualization
    Download  www.geovista.psu.edu
    : Geographic information (e.g., locations, networks, and nearest neighbors) are unique and different from other aspatial attributes (e.g., population, sales, or income). It is a ch...
    Diansheng Guo, Mark Gahegan
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    JKM
    2006
    135views more  JKM 2006»
    Learning from the Mars Rover Mission: scientific discovery, learning and memory
    13 years 9 months ago
    Learning from the Mars Rover Mission: scientific discovery, learning and memory
    Download  ti.arc.nasa.gov
    Purpose Knowledge management for space exploration is part of a multi-generational effort. Each mission builds on knowledge from prior missions, and learning is the first step in ...
    Charlotte Linde
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    JLP
    2006
    138views more  JLP 2006»
    Algebraic-coalgebraic specification in CoCasl
    13 years 9 months ago
    Algebraic-coalgebraic specification in CoCasl
    Download  www.informatik.uni-bremen.de
    We introduce CoCasl as a light-weight but expressive coalgebraic extension of the algebraic specification language Casl. CoCasl allows the nested combination of algebraic datatype...
    Till Mossakowski, Lutz Schröder, Markus Rogge...
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    JMIV
    2006
    91views more  JMIV 2006»
    Harmonic Embeddings for Linear Shape Analysis
    13 years 9 months ago
    Harmonic Embeddings for Linear Shape Analysis
    Download  www.ece.gatech.edu
    We present a novel representation of shape for closed contours in R2 or for compact surfaces in R3 explicitly designed to possess a linear structure. This greatly simplifies linear...
    Alessandro Duci, Anthony J. Yezzi, Stefano Soatto,...
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    JMLR
    2006
    140views more  JMLR 2006»
    Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
    13 years 9 months ago
    Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
    Download  jmlr.csail.mit.edu
    The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in line...
    Masashi Sugiyama
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