Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Ying-Ho Liu"'
Publikováno v:
Symmetry, Vol 14, Iss 12, p 2473 (2022)
Blockchain technology has recently attracted tremendous interest due to its potential to revolutionize the industry by achieving decentralization while increasing the number of data sources, transparency, reliability, auditability, and trustworthines
Externí odkaz:
https://doaj.org/article/41d76791d4a6442d8bf3824d5056c91c
Autor:
Ying-Ho Liu, 劉英和
97
Many previously proposed methods of object recognition use the salient regions of the objects to improve their robustness to distortion and occlusion. The methods based on salient regions inevitably encounter the difficulties if several diffe
Many previously proposed methods of object recognition use the salient regions of the objects to improve their robustness to distortion and occlusion. The methods based on salient regions inevitably encounter the difficulties if several diffe
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/05470329633590688290
Autor:
Ying-Ho Liu, Chia-Yu Kuo
Publikováno v:
The Journal of Supercomputing.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95905a408d509691ae4ccf356fc43e3e
https://doi.org/10.1007/s11227-023-05376-z
https://doi.org/10.1007/s11227-023-05376-z
Autor:
Ying-Ho Liu, Huei-Yu Fan
Publikováno v:
Applied Intelligence. 51:2622-2650
In the literature, univariate uncertain data has a quantitative interval for each attribute in each transaction, which is accompanied by a probability density function indicating the probability that each value in the interval exists and appears. To
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27a0cd49816a5ffd217174c4caa7ac55
https://doi.org/10.1007/s10489-020-01911-0
https://doi.org/10.1007/s10489-020-01911-0
Autor:
Ying-Ho Liu1 daxliu@gms.ndhu.edu.tw
Publikováno v:
NTU Management Review. 2017 supplement, Vol. 27, p29-61. 33p.
Autor:
Ying-Ho Liu
Publikováno v:
Intelligent Data Analysis. 18:653-676
In this paper, we propose mining maximal frequent patterns from univariate uncertain data. Univariate uncertain data refers to cases where each attribute in a transaction is associated with a quantitative interval and a probability density function t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3889a06f629b39d72519f17b2e4349b
https://doi.org/10.3233/ida-140662
https://doi.org/10.3233/ida-140662
Publikováno v:
Journal of Systems and Software. 86:1603-1612
Inter-sequence pattern mining can find associations across several sequences in a sequence database, which can discover both a sequential pattern within a transaction and sequential patterns across several different transactions. However, inter-seque
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e044b05006bbb522581e6b03ddae33d0
https://doi.org/10.1016/j.jss.2013.02.010
https://doi.org/10.1016/j.jss.2013.02.010
Autor:
Chun-Sheng Wang, Ying-Ho Liu
Publikováno v:
Journal of Systems and Software. 86:759-778
In this paper, we propose a new algorithm called CUP-Miner (Constrained Univariate Uncertain Data Pattern Miner) for mining frequent patterns from univariate uncertain data under user-specified constraints. The discovered frequent patterns are called
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c39390cb67551469bcd55034e803b41
https://doi.org/10.1016/j.jss.2012.11.020
https://doi.org/10.1016/j.jss.2012.11.020
Autor:
Ying-Ho Liu
Publikováno v:
Applied Intelligence. 39:315-344
In this paper, we propose mining frequent patterns from univariate uncertain data streams, which have a quantitative interval for each attribute in a transaction and a probability density function indicating the possibilities that the values in the i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e4ae857b9d6ce5d3e1802e2fbdedaf1
https://doi.org/10.1007/s10489-012-0415-3
https://doi.org/10.1007/s10489-012-0415-3
Publikováno v:
Computer Vision and Image Understanding. 116:854-867
The existing object recognition methods can be classified into two categories: interest-point-based and discriminative-part-based. The interest-point-based methods do not perform well if the interest points cannot be selected very carefully. The perf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d76aebf3605151bd293f9189771de93
https://doi.org/10.1016/j.cviu.2012.03.007
https://doi.org/10.1016/j.cviu.2012.03.007