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    » Kernel density estimation with adaptive varying window size
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    PRL
    2002
    106views more  PRL 2002»
    Kernel density estimation with adaptive varying window size
    13 years 4 months ago
    Kernel density estimation with adaptive varying window size
    Download  personal.systemsbiology.net
    A new method of kernel density estimation with a varying adaptive window size is proposed. It is based on the socalled intersection of confidence intervals (ICI) rule. Several exa...
    Vladimir Katkovnik, Ilya Shmulevich
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    IJCNN
    2007
    IEEE
    131views Neural Networks» more  IJCNN 2007»
    Probability Density Function Estimation Using Orthogonal Forward Regression
    13 years 11 months ago
    Probability Density Function Estimation Using Orthogonal Forward Regression
    Download  users.ecs.soton.ac.uk
    — Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression tec...
    Sheng Chen, Xia Hong, Chris J. Harris
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    EMMCVPR
    2009
    Springer
    130views Computer Vision» more  EMMCVPR 2009»
    Image Filtering Driven by Level Curves
    13 years 11 months ago
    Image Filtering Driven by Level Curves
    Download  www.cise.ufl.edu
    This paper presents an approach to image filtering that is driven by the properties of the iso-valued level curves of the image and their relationship with one another. We explore...
    Ajit Rajwade, Arunava Banerjee, Anand Rangarajan
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    CN
    2008
    280views more  CN 2008»
    Joint transmit power and physical carrier sensing adaptation based on loss differentiation for high density IEEE 802.11 WLAN
    13 years 4 months ago
    Joint transmit power and physical carrier sensing adaptation based on loss differentiation for high density IEEE 802.11 WLAN
    Download  www.ee.washington.edu
    In high density (HD) IEEE 802.11 WLAN, packet loss can occur due to co-channel interference (asynchronous interference) or collisions (synchronous interference). In order to effec...
    Hui Ma, Jing Zhu, Sumit Roy, Soo Young Shin
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    SDM
    2007
    SIAM
    198views Data Mining» more  SDM 2007»
    Learning from Time-Changing Data with Adaptive Windowing
    13 years 6 months ago
    Learning from Time-Changing Data with Adaptive Windowing
    Download  siam.org
    We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
    Albert Bifet, Ricard Gavaldà
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