Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Xiangju Qin"'
Publikováno v:
Applied Sciences, Vol 12, Iss 4, p 1920 (2022)
Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deplo
Externí odkaz:
https://doaj.org/article/08aacb26738c4ca4bb2799d2336b712a
Autor:
J Lampe, Krister Wennerberg, Prson Gautam, Julia P. Vainonen, Srikar G. Nagelli, Xiangju Qin, Tero Aittokallio, Jukka Westermarck, Juha Klefström
Publikováno v:
Cancer Research. 80:P6-10
Triple negative breast cancer (TNBC) is a highly aggressive type of breast cancer with poor prognosis that accounts approximately for 15% of breast cancer cases. The lack of targetable hormonal receptors or HER2/ErbB2 makes TNBC therapeutically chall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd4e70eae9e8df615468ad17f46e8a2b
https://doi.org/10.1158/1538-7445.sabcs19-p6-10-28
https://doi.org/10.1158/1538-7445.sabcs19-p6-10-28
Publikováno v:
IJCAI
Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparsely observed. A standard solution is matrix factorization, which predic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f2eec0bc09c0d9d704bfff6207b3b52
https://doi.org/10.24963/ijcai.2019/454
https://doi.org/10.24963/ijcai.2019/454
Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals. Scaling up the posterior inference for massive-scale matrices is challengi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::140ae0a9dad75948ccbb9aa2e99c7934
https://aaltodoc.aalto.fi/handle/123456789/37722
https://aaltodoc.aalto.fi/handle/123456789/37722
Publikováno v:
Journal of Intelligent Information Systems. 40:405-430
Many studies on streaming data classification have been based on a paradigm in which a fully labeled stream is available for learning purposes. However, it is often too labor-intensive and time-consuming to manually label a data stream for training.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::167da7ae392748e383bb6aadb2843537
https://doi.org/10.1007/s10844-012-0231-6
https://doi.org/10.1007/s10844-012-0231-6
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319290089
MSM/MUSE/SenseML
MSM/MUSE/SenseML
Collaborations such as Wikipedia are a key part of the value of the modern Internet. At the same time there is concern that these collaborations are threatened by high levels of member withdrawal. In this paper we borrow ideas from topic analysis to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::531ab2ad2664042597c41b6141819f76
https://doi.org/10.1007/978-3-319-29009-6_3
https://doi.org/10.1007/978-3-319-29009-6_3
Publikováno v:
Social Networks. 43
The proliferation of online communities has attracted much attention to modelling user behaviour in terms of social interaction, language adoption and contribution activity. Nevertheless, when applied to large-scale and cross-platform behavioural dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::856fc9f7101aafc4b6f0e1c2d2571803
http://ora.ox.ac.uk/objects/uuid:b2d28614-67b0-4166-b4ea-daa283cccefe
http://ora.ox.ac.uk/objects/uuid:b2d28614-67b0-4166-b4ea-daa283cccefe
Publikováno v:
Web-Age Information Management ISBN: 9783642142451
WAIM
WAIM
Associative classifiers are relatively easy for people to understand and often outperform decision tree learners on many classification problems. Existing associative classifiers only work with certain data. However, data uncertainty is prevalent in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d4ded600fd44765ffb052f015f14698
https://doi.org/10.1007/978-3-642-14246-8_66
https://doi.org/10.1007/978-3-642-14246-8_66
Autor:
Xiangju Qin, Yang Zhang
Publikováno v:
CSSE (4)
The aim of topic tracking is to monitor the stream of news stories to find additional stories on a topic that was identified by several sample stories. In this paper, we adopt majority voting to ensemble several topic tracking systems. The experiment
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f489f03e7957aa37feb806cdffea3b7
https://doi.org/10.1109/csse.2008.984
https://doi.org/10.1109/csse.2008.984
Publikováno v:
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Computational Science – ICCS 2020
International Conference on Computational Science (ICCS 2020)
Lecture Notes in Computer Science ISBN: 9783030504328
ICCS (6)
Lecture Notes in Computer Science-Computational Science – ICCS 2020
International Conference on Computational Science (ICCS 2020)
Lecture Notes in Computer Science ISBN: 9783030504328
ICCS (6)
Matrix factorization is a very common machine learning technique in recommender systems. Bayesian Matrix Factorization (BMF) algorithms would be attractive because of their ability to quantify uncertainty in their predictions and avoid over-fitting,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f92789c9212c3e35a476320f018e169e