Zobrazeno 1 - 10
of 91
pro vyhledávání: '"S. Chow"'
Publikováno v:
IEEE Transactions on Industrial Informatics. 18:4477-4487
Missing data widely exist in industrial processes and lead to difficulties in modelling, monitoring, fault diagnosis, and control. In this paper, we propose a nonlinear method to handle the missing data problem in the offline modelling stage or/and t
Autor:
Tommy W. S. Chow, Jianghong Ma
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:315-329
Multilabel learning has been extensively studied in the past years, as it has many applications in different domains. It aims at annotating the labels for unseen data according to training data, which are often high dimensional in both instance and f
Autor:
Xiaolei Lu, Tommy W. S. Chow
Existing disambiguation strategies for partial structured output learning just cannot generalize well to solve the problem that there are some candidates that can be false positive or similar to the ground-truth label. In this article, we propose a n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7970dbe636bbc2094e192fd45084d56
http://arxiv.org/abs/2209.09410
http://arxiv.org/abs/2209.09410
Autor:
Tommy W. S. Chow, Yang Zhao
Publikováno v:
Information Sciences. 560:283-306
Current research on subset selection for opinion analysis assumes that their methods can retrieve the opinions expressed in documents from general text features. However, such relaxed conditions can hardly maintain the performance of the analysis in
Autor:
Tommy W. S. Chow, Yang Zhao
Publikováno v:
Neural Computing and Applications. 33:12381-12396
Along with online social media’s prosperity, the amount of user-generated reviews dramatically increases. The kinds of text-based user-generated content are conducive to estimating public sentiments. Many sentiment analysis works are based on the a
Autor:
Christopher G. Filippi, John A. Boockvar, Daniel S. Chow, Deepak Khatri, Avraham B Zlochower, Peter Chang
Publikováno v:
Neuroimaging Clinics of North America. 30:493-503
Deep learning represents end-to-end machine learning in which feature selection from images and classification happen concurrently. This articles provides updates on how deep learning is being applied to the study of glioma and its genetic heterogene
Publikováno v:
IEEE Transactions on Industrial Informatics. 16:6750-6759
This paper presents a new framework for matching clothes by considering item in-between compatibility. In contrast to the use of visual features of clothing items, we only utilized their textual descriptions, i.e., title sentences, to constitute the
Autor:
Alex Luk, Min-Ying Su, Vivian Youngjean Park, Jeon-Hor Chen, Kai Ting Chang, Yang Zhang, Daniel S. Chow, Tiffany C. Kwong, Peter Chang, Min Jung Kim, Siwa Chan
Publikováno v:
Acad Radiol
Rationale and Objectives Breast segmentation using the U-net architecture was implemented and tested in independent validation datasets to quantify fibroglandular tissue volume in breast MRI. Materials and Methods Two datasets were used. The training
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 68:3128-3136
This paper proposes a new bearing fault detection framework that is based on multivariate statistical process control methods. In this framework, historical offline normal data are used to train the models and calculate the control limits of the moni
Autor:
Tommy W. S. Chow, Hadrien Van Lierde
Publikováno v:
Information Sciences. 496:212-224
Existing graph-based methods for extractive document summarization represent sentences of a corpus as the nodes of a graph or a hypergraph in which edges depict relationships of lexical similarity between sentences. Such approaches fail to capture se