Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Jianping Yin"'
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:7755-7764
In online learning, the dynamic regret metric chooses the reference oracle that may change over time, while the typical (static) regret metric assumes the reference solution to be constant over the whole time horizon. The dynamic regret metric is par
Autor:
Siqi Wang, Yijie Zeng, Guang Yu, Zhen Cheng, Xinwang Liu, Sihang Zhou, En Zhu, Marius Kloft, Jianping Yin, Qing Liao
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. :1-18
Autor:
Siqi Wang, Jiyuan Liu, Guang Yu, Xinwang Liu, Sihang Zhou, En Zhu, Yuexiang Yang, Jianping Yin, Wenjing Yang
Publikováno v:
IEEE transactions on neural networks and learning systems.
Anomaly detection (AD), which models a given normal class and distinguishes it from the rest of abnormal classes, has been a long-standing topic with ubiquitous applications. As modern scenarios often deal with massive high-dimensional complex data s
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 31:1351-1362
Multiple kernel clustering (MKC) has been intensively studied during the last few decades. Even though they demonstrate promising clustering performance in various applications, existing MKC algorithms do not sufficiently consider the intrinsic neigh
Publikováno v:
Information Fusion. 53:145-154
Multiple kernel clustering (MKC) is an important research topic during the last few decades. It optimally combines a group of pre-specified base kernels to improve clustering performance. Though demonstrating promising performance in various applicat
Autor:
Xinwang Liu, En Zhu, Luyan Liu, Xin Xu, Siqi Wang, Sihang Zhou, Siwei Wang, Jianping Yin, Qiyuan Ou
Publikováno v:
IEEE transactions on neural networks and learning systems.
Multiple kernel clustering (MKC) has recently achieved remarkable progress in fusing multisource information to boost the clustering performance. However, the O( n²) memory consumption and O( n³ ) computational complexity prohibit these methods fro
Publikováno v:
Multimedia Tools and Applications. 78:29509-29527
A smart environment is one of the application scenarios of the Internet of Things (IoT). In order to provide a ubiquitous smart environment for humans, a variety of technologies are developed. In a smart environment system, sound event detection is o
Complex categorical data is often hierarchically coupled with heterogeneous relationships between attributes and attribute values and the couplings between objects. Such value-to-object couplings are heterogeneous with complementary and inconsistent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29af7f4e9210460b1daca2601fc229eb
http://arxiv.org/abs/2007.10720
http://arxiv.org/abs/2007.10720
Autor:
Dinggang Shen, Miaomiao Li, Tongliang Liu, Xinzhong Zhu, Jianping Yin, Lei Wang, En Zhu, Wen Gao, Xinwang Liu, Marius Kloft
Publikováno v:
IEEE Trans Pattern Anal Mach Intell
Multiple kernel clustering (MKC) algorithms optimally combine a group of pre-specified base kernel matrices to improve clustering performance. However, existing MKC algorithms cannot efficiently address the situation where some rows and columns of ba
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26e7f000966039c296ff6858d01e26f2
https://hdl.handle.net/10453/147097
https://hdl.handle.net/10453/147097
Publikováno v:
Soft Computing. 22:3603-3619
Learning from categorical data is a critical yet challenging task. Current research focuses on either leveraging the complex interaction between and within categorical values to generate a numerical representation, or designing a model that can tackl