Results 31 to 40 of about 200,311 (278)
Machine Learning-Based Forecasting of Bitcoin Price Movements
Proceedings of the International Conference on Applied Innovations in ITIn the volatile realm of cryptocurrency markets, this research explores the intricate dance of Bitcoin price dynamics through the lens of machine learning. Employing a multifaceted approach, we harness the power of Long Short-Term Memory (LSTM) networks,
Darko Angelovski +4 more
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Comparing ensemble learning algorithms and severity of illness scoring systems in cardiac intensive care units: a retrospective study [PDF]
Einstein (São Paulo)Objective: Logistic Regression has been used traditionally for the development of most predictor tools of intensive care unit mortality. The purpose of this study is to combine shared risk factors between patients undergoing cardiac surgery and intensive
Beatriz Nistal-Nuño
doaj +1 more source
International Journal of Machine Learning and CyberneticsAbstract This paper presents a computationally efficient variant of Gradient Boosting (GB) for multi-class classification and multi-output regression tasks. Standard GB uses a 1-vs-all strategy for classification tasks with more than two classes. This strategy entails that one tree per class and iteration has to be trained.
Seyedsaman Emami +1 more
openaire +3 more sources
Boosting Additive Models using Component-wise P-Splines [PDF]
, 2007 We consider an efficient approximation of Bühlmann & Yu’s L2Boosting algorithm with component-wise smoothing splines. Smoothing spline base-learners are replaced by P-spline base-learners which yield similar prediction errors but are more advantageous ...
Hothorn, Torsten, Schmid, Matthias
core +1 more source
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
REGRESSION-BASED PREDICTION OF ANXIETY SEVERITY [PDF]
Carpathian Journal of Electrical EngineeringThe present study explores the use of machine learning to predict self-reported anxiety levels based on demographic, behavioral, and physiological data.
Bogdan CHIS +3 more
doaj +1 more source
Sequential Training of Neural Networks With Gradient Boosting
IEEE Access, 2023 This paper presents a novel technique based on gradient boosting to train the final layers of a neural network (NN). Gradient boosting is an additive expansion algorithm in which a series of models are trained sequentially to approximate a given function.
Seyedsaman Emami, Gonzalo Martinez-Munoz
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BuildingsThe standard approach for testing ordinary concrete compressive strength (CS) is to cast samples and test them after different curing times. However, testing adds cost and time to projects, and, therefore, construction sites experience delays.
Fei Zhu +3 more
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QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
Applied Sciences, 2023 The Quick UDP Internet Connections (QUIC) protocol provides advantages over traditional TCP, but its encryption functionality reduces the visibility for operators into network traffic.
Sultan Almuhammadi +2 more
doaj +1 more source