Results 21 to 30 of about 8,972,433 (325)
Exploring and Exploiting Conditioning of Reinforcement Learning Agents
IEEE Access, 2020 The outcome of Jacobian singular values regularization was studied for supervised learning problems. In supervised learning settings for linear and nonlinear networks, Jacobian regularization allows for faster learning.
Arip Asadulaev +3 more
doaj +1 more source
Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint
Arthritis Research & Therapy, 2022 Rheumatoid arthritis is an autoimmune condition that predominantly affects the synovial joints, causing joint destruction, pain, and disability. Historically, the standard for measuring the long-term efficacy of disease-modifying antirheumatic drugs has ...
Alix Bird +8 more
doaj +1 more source
Machine learning-based prediction of a BOS reactor performance from operating parameters [PDF]
, 2020 A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of ...
Li, Zushu +2 more
core +1 more source
Thumbs up? Sentiment Classification using Machine Learning Techniques [PDF]
Conference on Empirical Methods in Natural Language Processing, 2002 We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform ...
B. Pang +2 more
semanticscholar +1 more source
Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Purpose Treatment planning for head‐and‐neck (H&N) cancer, in particular oropharynx, nasopharynx, and paranasal sinus cases, at our center requires noncoplanar proton beams due to the complexity of the anatomy and target location. Targeting accuracy for all beams is carefully evaluated by using image guidance before delivering proton beam ...
Nrusingh C. Biswal +5 more
wiley +1 more source
Machine learning as ecology [PDF]
Journal of Physics A: Mathematical and Theoretical, 2020 Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms - including Support Vector Machines (SVMs) -- have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecological invasions, and benchmark ...
Owen Howell +4 more
openaire +5 more sources
Optimization Methods for Large-Scale Machine Learning [PDF]
SIAM Review, 2016 This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications.
L. Bottou, Frank E. Curtis, J. Nocedal
semanticscholar +1 more source
De Computis, 2023 In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance.
Mohammad Mustafa Taye
semanticscholar +1 more source
Image quality improvement in low‐dose chest CT with deep learning image reconstruction
Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Objectives To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low‐dose chest CT in comparison with 40% adaptive statistical iterative reconstruction‐Veo (ASiR‐V40%) algorithm. Methods This retrospective study included 86 patients who underwent low‐dose CT for lung cancer screening ...
Qian Tian +7 more
wiley +1 more source
Using Proximity Graph Cut for Fast and Robust Instance-Based Classification in Large Datasets
Complexity, 2021 K-nearest neighbours (kNN) is a very popular instance-based classifier due to its simplicity and good empirical performance. However, large-scale datasets are a big problem for building fast and compact neighbourhood-based classifiers. This work presents
Stanislav Protasov, Adil Mehmood Khan
doaj +1 more source