Hybrid reinforcement learning with expert state sequences
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Any notion of "closeness" in pattern matching should have the property that if A is close to B, and B is close to C, then A is close to C. Traditionally, this property is attained because of the triangle inequality (d(A, C) ≤ d(A, B) + d(B, C), where d represents a notion of distance). However, the full power of the triangle inequality is not needed for this property to hold. Instead, a "relaxed triangle inequality" suffices, of the form d(A, C) < c(d(A, B) + d(B, C)), where c is a constant that is not too large. In this paper, we show that one of the measures used for distances between shapes in (an experimental version of) IBM's QBIC1 ("Query by Image Content") system (Niblack et al., 1993) satisfies a relaxed triangle inequality, although it does not satisfy the triangle inequality.
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Imran Nasim, Melanie Weber
SCML 2024
P.C. Yue, C.K. Wong
Journal of the ACM
Wang Zhang, Subhro Das, et al.
ICASSP 2025