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    IJRR
    2007
    186views more  IJRR 2007»
    Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
    13 years 6 months ago
    Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
    Download  www.cs.washington.edu
    Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract a person’s activities and significant plac...
    Lin Liao, Dieter Fox, Henry A. Kautz
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    IHI
    2010
    219views Healthcare» more  IHI 2010»
    Conditional random fields for activity recognition in smart environments
    13 years 1 months ago
    Conditional random fields for activity recognition in smart environments
    Download  www.eecs.wsu.edu
    One of the most common functions of smart environments is to monitor and assist older adults with their activities of daily living. Activity recognition is a key component in this...
    Ehsan Nazerfard, Barnan Das, Lawrence B. Holder, D...
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    AAAI
    2010
    186views Intelligent Agents» more  AAAI 2010»
    Fast Conditional Density Estimation for Quantitative Structure-Activity Relationships
    13 years 6 months ago
    Fast Conditional Density Estimation for Quantitative Structure-Activity Relationships
    Download  wwwkramer.in.tum.de
    Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to qua...
    Fabian Buchwald, Tobias Girschick, Eibe Frank, Ste...
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    ICCV
    2003
    IEEE
    201views Computer Vision» more  ICCV 2003»
    Conditional Feature Sensitivity: A Unifying View on Active Recognition and Feature Selection
    14 years 8 months ago
    Conditional Feature Sensitivity: A Unifying View on Active Recognition and Feature Selection
    Download  www.caip.rutgers.edu
    The objective of active recognition is to iteratively collect the next "best" measurements (e.g., camera angles or viewpoints), to maximally reduce ambiguities in recogn...
    Xiang Sean Zhou, Dorin Comaniciu, Arun Krishnan
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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 6 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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