Results 41 to 50 of about 1,243,089 (305)
Judgment and Decision Making, 2023 Short-sighted decisions can have devastating consequences, and teaching people to make their decisions in a more far-sighted way is challenging. Previous research found that reflecting on one’s behavior can boost learning from success and failure.
Frederic Becker +4 more
doaj +1 more source
Boosting Few-Shot Visual Learning With Self-Supervision [PDF]
IEEE International Conference on Computer Vision, 2019 Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data. Few-shot learning aims for optimization methods and models that can learn efficiently to recognize patterns ...
Spyros Gidaris +4 more
semanticscholar +1 more source
Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)
Diagnostics, 2021 Diabetes mellitus (DM) is a severe chronic disease that affects human health and has a high prevalence worldwide. Research has shown that half of the diabetic people throughout the world are unaware that they have DM and its complications are increasing,
Derara Duba Rufo +3 more
semanticscholar +1 more source
Popular Ensemble Methods: An Empirical Study
, 2011 An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of
Maclin, R., Opitz, D.
core +1 more source
Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus
MathematicsEnsemble learning and data fusion techniques play a crucial role in modern machine learning, enhancing predictive performance, robustness, and generalization.
Ke-Lin Du +4 more
doaj +1 more source
PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS
Mağallaẗ Al-kūfaẗ Al-handasiyyaẗSoftware plays a vital role in all aspects of our daily lives, specifically in the fields of medicine and industry. In order to design high-quality and reliable software and avoid risks resulting from software errors, including physical and human errors,
Raghda Azad Hasan, Ibrahim Ahmed Saleh
doaj +1 more source
Generalized Boosting Algorithms for Convex Optimization [PDF]
, 2011 Boosting is a popular way to derive powerful learners from simpler hypothesis classes. Following previous work (Mason et al., 1999; Friedman, 2000) on general boosting frameworks, we analyze gradient-based descent algorithms for boosting with respect to ...
Bagnell, J. Andrew, Grubb, Alexander
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Learning Nonlinear Functions Using Regularized Greedy Forest
, 2013 We consider the problem of learning a forest of nonlinear decision rules with general loss functions. The standard methods employ boosted decision trees such as Adaboost for exponential loss and Friedman's gradient boosting for general loss.
Johnson, Rie, Zhang, Tong
core +2 more sources
uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers [PDF]
, 2013 The use of multivariate classifiers, especially neural networks and decision trees, has become commonplace in particle physics. Typically, a series of classifiers is trained rather than just one to enhance the performance; this is known as boosting. This
Stevens, Justin, Williams, Mike
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Introduction Neuroblastoma (NB) with central nervous system (CNS) metastases is rare at diagnosis, but occurs more often during relapse/progression. Patients with CNS metastases face a dismal prognosis, with no standardized curative treatment available.
Vicente Santa‐Maria Lopez +13 more
wiley +1 more source