Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Kun Lu"'
Publikováno v:
Neurocomputing. 435:186-196
In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views and fuse them by considering each view’s dynamic contribution obtained
Publikováno v:
Future Generation Computer Systems. 116:209-219
An organisation wishing to conduct data analytics to support day-to-day decision making often needs a system to help analysts represent and maintain knowledge about research variables, datasets or analytical models, and effectively determine the best
Publikováno v:
Applied Intelligence. 51:5420-5439
Online learning is an important technical means for sketching massive real-time and high-speed data. Although this direction has attracted intensive attention, most of the literature in this area ignore the following three issues: (1) they think litt
Autor:
Shanshuang Shi, Dongyi Li, Wenlong Zhao, Kun Lu, Zhang Yu, Junwei Li, Yang Songzhu, Yong Cheng
Publikováno v:
Nuclear Engineering and Technology. 52:2630-2637
The structure design of divertor Multi-Functional Maintenance Platform (MFMP) actuated by hydraulic system for China Fusion Engineering Test Reactor (CFETR) was introduced in this paper. The model of MFMP was established according to maintenance requ
Publikováno v:
Neural Computing and Applications. 33:6039-6064
For multi-label learning, the specific features are extracted from the instances under the supervised of class label is meaningful, and the "purified" feature representation can also be shared with other features during learning process. Besides, it
Real world datasets often contain noisy labels, and learning from such datasets using standard classification approaches may not produce the desired performance. In this paper, we propose a Gaussian Mixture Discriminant Analysis (GMDA) with noisy lab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53b54cddc1621831e48a784b8baab657
Multi-view learning attempts to generate a model with a better performance by exploiting the consensus and/or complementarity among multi-view data. However, in terms of complementarity, most existing approaches only can find representations with sin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77542f973f199bbe08d56914733d1b7f
Multi-view learning accomplishes the task objectives of classification by leverag-ing the relationships between different views of the same object. Most existing methods usually focus on consistency and complementarity between multiple views. But not
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::824b8b67f82a830c5a47f021d621df9a
Publikováno v:
Online Information Review. 44:258-277
PurposeThe purpose of this paper is to explore topics of Facebook posts created by public libraries using the bi-term topic model, and examine the relationships between types of topics and user engagement. The authors further investigated the effects
Publikováno v:
Neural Computing and Applications. 32:10403-10422
Over recent decades have witnessed considerable progress in whether multi-task learning or multi-view learning, but the situation that consider both learning scenes simultaneously has received not too much attention. How to utilize multiple views lat