Results 91 to 100 of about 1,080,302 (365)

Query Optimization Algorithm Based on Learning to Rank [PDF]

Jisuanji kexue
Query optimization is a key aspect of relational databases.In the traditional query optimization process,cardinality estimation of join and filter operations in a query is usually required in order to obtain a better execution plan.However,due to the ...
YU Yang, PENG Yuwei
doaj   +1 more source

Neural Feature Selection for Learning to Rank [PDF]

, 2021
AbstractLEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items. In the past few years, neural LETOR approaches have become a competitive alternative to traditional ones like LambdaMART. However, neural architectures performance grew proportionally to their
Purpura A., Buchner K., Silvello G., Susto G. A.   +3 more
openaire   +2 more sources

Detecting homologous recombination deficiency for breast cancer through integrative analysis of genomic data

Molecular Oncology, EarlyView.
This study develops a semi‐supervised classifier integrating multi‐genomic data (1404 training/5893 validation samples) to improve homologous recombination deficiency (HRD) detection in breast cancer. Our method demonstrates prognostic value and predicts chemotherapy/PARP inhibitor sensitivity in HRD+ tumours.
Rong Zhu, Katherine Eason, Suet‐Feung Chin, Paul A. W. Edwards, Raquel Manzano Garcia, Richard Moulange, Jia Wern Pan, Soo Hwang Teo, Sach Mukherjee, Maurizio Callari, Carlos Caldas, Stephen‐John Sammut, Oscar M. Rueda   +12 more
wiley   +1 more source

Automatic Morpheme-based Distractors Generation for Fill-in-the-Blank Questions using Listwise Learning-To-Rank Method for Agglutinative Language

Engineering Science and Technology, an International Journal, 2022
Automatic question generation facilitates the smart assessment for the evaluator to assess the student skills. Several methods were proposed to generate distractors for non-factoid cloze question using different similarity measures. This study presents a
Shanthi Murugan, Balasundaram Sadhu Ramakrishnan   +1 more
doaj   +1 more source

Regression and Learning to Rank Aggregation for User Engagement Evaluation

, 2014
User engagement refers to the amount of interaction an instance (e.g., tweet, news, and forum post) achieves. Ranking the items in social media websites based on the amount of user participation in them, can be used in different applications, such as ...
Moradi, Pooya, Shakery, Azadeh, Zamani, Hamed   +2 more
core   +1 more source

Data‐driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

Molecular Oncology, EarlyView.
This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...
Mona Nourbakhsh, Nikola Tom, Anna Schrøder Lassen, Helene Brasch Lind Petersen, Ulrik Kristoffer Stoltze, Karin Wadt, Kjeld Schmiegelow, Matteo Tiberti, Elena Papaleo   +8 more
wiley   +1 more source

Learning to Rank Retargeted Images [PDF]

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Image retargeting techniques that adjust images into different\ud sizes have attracted much attention recently. Objective\ud quality assessment (OQA) of image retargeting results\ud is often desired to automatically select the best results. Existing\ud OQA methods output an absolute score for each retargeted\ud image and use these scores to compare ...
Yang Chen, Yong-Jin Liu, Yu-Kun Lai
openaire   +3 more sources

RankDNN: Learning to Rank for Few-Shot Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic.
Guo, Qianyu, Gong, Hongtong, Wei, Xujun, Fu, Yanwei, Ge, Weifeng, Yu, Yizhou, Zhang, Wenqiang   +6 more
openaire   +2 more sources

Systematic profiling of cancer‐fibroblast interactions reveals drug combinations in ovarian cancer

Molecular Oncology, EarlyView.
Fibroblasts, cells in the tumor environment, support ovarian cancer cell growth and alter morphology and drug response. We used fibroblast and cancer cell co‐culture models to test 528 drugs and discovered new drugs for combination treatment. We showed that adding Vorinostat or Birinapant to standard chemotherapy may improve drug response, suggesting ...
Greta Gudoityte, Osheen Sharma, Laura Leuenberger, Emelie Wallin, Josefin Fernebro, Päivi Östling, Rebecka Bergström, Johan Lindberg, Ulrika Joneborg, Olli Kallioniemi, Brinton Seashore‐Ludlow   +10 more
wiley   +1 more source

Towards intelligent geospatial data discovery: a machine learning framework for search ranking

International Journal of Digital Earth, 2018
Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension (e.g. popularity and release date).
Yongyao Jiang, Yun Li, Chaowei Yang, Fei Hu, Edward M. Armstrong, Thomas Huang, David Moroni, Lewis J. McGibbney, Christopher J. Finch   +8 more
doaj   +1 more source