id: 05942644
dt: a
an: 05942644
au: Xu, Tao; Gondra, Iker; Chiu, David
ti: Adaptive kernel diverse density estimate for multiple instance learning.
so: Perner, Petra (ed.), Machine learning and data mining in pattern
    recognition. 7th international conference, MLDM 2011, New York, NY,
    USA, August 30 ‒ September 3, 2011. Proceedings. Berlin: Springer
    (ISBN 978-3-642-23198-8/pbk). Lecture Notes in Computer Science 6871.
    Lecture Notes in Artificial Intelligence, 185-198 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-23199-5_14
ab: Summary: We present AKDDE, an adaptive kernel diverse density estimate
    scheme for multiple instance learning. AKDDE revises the definition of
    diverse density as the kernel density estimate of diverse positive
    bags. We show that the AKDDE is inversely proportional to the least
    bound that contains at least one instance from each positive bag. In
    order to incorporate the influence of negative bags an objective
    function is constructed as the difference between the AKDDE of positive
    bags and the kernel density estimate of negative ones. This scheme is
    simple in concept and has better properties than other MIL methods. We
    validate AKDDE on both synthetic and real-world benchmark MIL datasets.
rv: