Results 51 to 60 of about 1,099,722 (325)
Learning to Rank Using Localized Geometric Mean Metrics
, 2017 Many learning-to-rank (LtR) algorithms focus on query-independent model, in which query and document do not lie in the same feature space, and the rankers rely on the feature ensemble about query-document pair instead of the similarity between query ...
King, Irwin, Lyu, Michael, Su, Yuxin
core +1 more source
Learning to Rank Learning Curves
, 2020 Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations. In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training. In contrast to
Wistuba, Martin, Pedapati, Tejaswini
openaire +2 more sources
Extraction of Effective Textual and Semantic Features in Learning to Rank for Web Document Retrieval
Iranian Journal of Information Processing & Management, 2021 Ranking algorithms, as the core of web search systems, are responsible for finding and ranking the most relevant documents to user information needs from the crawled and indexed corpus.
Mohaddeseh Mahjoob +4 more
doaj
Image Retrieval Based on Learning to Rank and Multiple Loss
ISPRS International Journal of Geo-Information, 2019 Image retrieval applying deep convolutional features has achieved the most advanced performance in most standard benchmark tests. In image retrieval, deep metric learning (DML) plays a key role and aims to capture semantic similarity information carried ...
Lili Fan +4 more
doaj +1 more source
Early Exit Strategies for Learning-to-Rank Cascades
IEEE Access, 2023 The ranking pipelines of modern search platforms commonly exploit complex machine-learned models and have a significant impact on the query response time.
Francesco Busolin +5 more
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Introduction We developed MedSupport, a multilevel medication adherence intervention designed to address root barriers to medication adherence. This study sought to explore the feasibility and acceptability of the MedSupport intervention strategies to support a future full‐scale randomized controlled trial.
Elizabeth G. Bouchard +8 more
wiley +1 more source
Extracting Emotion Causes Using Learning to Rank Methods From an Information Retrieval Perspective
IEEE Access, 2019 Emotion cause extraction is a challenging task for the fine-grained emotion analysis. Even though a few studies have addressed the task using clause-level classification methods, most of them have partly ignored emotion-level context information.
Bo Xu +5 more
doaj +1 more source
Learning to Rank with BERT for Argument Quality Evaluation
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 The task of argument quality ranking, which identifies the quality of free text arguments, remains, to this day, a challenge. While most state-of-the-art initiatives use point-wise ranking methods and predict an absolute quality score for each argument ...
Charles-Olivier Favreau +2 more
doaj +1 more source
Learning to rank music tracks using triplet loss
, 2020 Most music streaming services rely on automatic recommendation algorithms to exploit their large music catalogs. These algorithms aim at retrieving a ranked list of music tracks based on their similarity with a target music track.
Peeters, Geoffroy +2 more
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Cyclophosphamide (CY) is associated with potentially fatal cardiotoxicity, yet no electrocardiographic indices have been established for early detection of CY‐induced cardiomyopathy. This study aimed to determine whether corrected QT interval (QTc) prolongation can predict early onset of CY‐related cardiac dysfunction in pediatric ...
Junpei Kawamura +5 more
wiley +1 more source