Search Results - "regularized stochastic bfgs algorithm"

RES: Regularized Stochastic BFGS Algorithm

Authors: Aryan Mokhtari Alejandro Ribeiro

Source: IEEE Transactions on Signal Processing. 62:6089-6104

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Machine Learning (stat.ML), 14. Life underwater, 02 engineering and technology, Mathematics - Optimization and Control, Machine Learning (cs.LG)

Access URL: http://arxiv.org/pdf/1401.7625
http://arxiv.org/abs/1401.7625

Regularized stochastic BFGS algorithm

Authors: Aryan Mokhtari Alejandro Ribeiro

Source: 2013 IEEE Global Conference on Signal and Information Processing. :1109-1112

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://arxiv.org/pdf/1401.7625v1
https://dblp.uni-trier.de/db/journals/corr/corr1401.html#MokhtariR14
https://www.seas.upenn.edu/~aribeiro/preprints/c_2013_mokhtari_ribeiro_a.pdf
https://arxiv.org/abs/1401.7625
https://ieeexplore.ieee.org/document/6737088/

RES: Regularized Stochastic BFGS Algorithm

Authors: Mokhtari, Aryan Ribeiro, Alejandro

Index Terms: Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1401.7625

An Improved Technique for Pneumonia Infected Patients Image Recognition Based on Combination Algorithm of Smooth Generalized Pinball SVM and Variational Autoencoders

Authors: Wachiraphong Ratiphaphongthon Wanida Panup Rabian Wangkeeree

Source: IEEE Access, Vol 10, Pp 107431-107445 (2022)

Subject Terms: generalized pinball loss, feature noise, Support vector machine, image recognition, regularized stochastic BFGS algorithm, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, TK1-9971

Access URL: https://doaj.org/article/f6a6cf36569c41d5bc6cc634a00638b2

Regularized stochastic BFGS algorithm.

Authors: Mokhtari, Aryan Ribeiro, Alejandro

Source: 2013 IEEE Global Conference on Signal & Information Processing; 2013, p1109-1112, 4p

From Initialization to Convergence: A Three-Stage Technique for Robust RBF Network Training.

Authors: Tsoulos, Ioannis G. Charilogis, Vasileios Tsalikakis, Dimitrios

Source: AI; Nov2025, Vol. 6 Issue 11, p280, 38p

Subject Terms: MACHINE learning, RADIAL basis functions, MATHEMATICAL optimization, REGRESSION analysis, PROCESS optimization, CLASSIFICATION algorithms

Efficient Reduction of Variances in Stochastic Spectral Conjugate Gradient Algorithm.

Authors: YIMER, SEIFU ENDRIS KUMAM, POOM CHAIPUNYA, PARIN

Source: Carpathian Journal of Mathematics; 2025, Vol. 41 Issue 3, p849-862, 14p

Subject Terms: CONVEX functions, CONJUGATE gradient methods, NONLINEAR equations, MACHINE learning, ALGORITHMS, GENERALIZATION

Piecewise just-in-time data recovering and fault detection method for time-varying wind power generation process with missing data.

Authors: Chang, Junyu Jing, Hua Chen, Xu et al.

Source: International Journal of Machine Learning & Cybernetics; Jun2025, Vol. 16 Issue 5, p3399-3412, 14p

A robust stochastic quasi-Newton algorithm for non-convex machine learning: A robust stochastic quasi-Newton algorithm...: H. Liu et al.

Authors: Liu, Hanger Liang, Yuqing Liu, Jinlan et al.

Source: Applied Intelligence; May2025, Vol. 55 Issue 7, p1-14, 14p

Subject Terms: COMPUTATIONAL mathematics, MACHINE learning, MATHEMATICAL optimization, CHECKS, ALGORITHMS, QUASI-Newton methods

Bolstering stochastic gradient descent with model building.

Authors: Birbil, Ş. İlker Martin, Özgür Onay, Gönenç et al.

Source: TOP (1134-5764); Oct2024, Vol. 32 Issue 3, p517-536, 20p

Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood's model.

Authors: Grzesiak, Wilhelm Zaborski, Daniel Szatkowska, Iwona et al.

Source: Animal Bioscience. Apr2021, Vol. 34 Issue 4, p770-782. 13p.

On the Inversion‐Free Newton's Method and Its Applications.

Authors: Chau, Huy N. Kirkby, J. Lars Nguyen, Dang H. et al.

Source: International Statistical Review; Aug2024, Vol. 92 Issue 2, p284-321, 38p

Subject Terms: NEWTON-Raphson method, ALGORITHMS

Newtonian Property of Subgradient Method with Optimization of Metric Matrix Parameter Correction.

Authors: Tovbis, Elena Krutikov, Vladimir Kazakovtsev, Lev

Source: Mathematics (2227-7390); Jun2024, Vol. 12 Issue 11, p1618, 27p

Subject Terms: SUBGRADIENT methods, NEWTON-Raphson method, DERIVATIVES (Mathematics), QUASI-Newton methods, SMOOTHNESS of functions, MATRIX inversion, NONSMOOTH optimization

A Hard-Constraint Wide-Body Physics-Informed Neural Network Model for Solving Multiple Cases in Forward Problems for Partial Differential Equations.

Authors: Chen, Simin Liu, Zhixiang Zhang, Wenbo et al.

Source: Applied Sciences (2076-3417); Jan2024, Vol. 14 Issue 1, p189, 21p

Subject Terms: PARTIAL differential equations, FINITE volume method, PHASE transitions, FINITE difference method, FINITE element method, BURGERS' equation, WAVE equation

On the Convergence Rate of Quasi-Newton Methods on Strongly Convex Functions with Lipschitz Gradient.

Authors: Krutikov, Vladimir Tovbis, Elena Stanimirović, Predrag et al.

Source: Mathematics (2227-7390); Dec2023, Vol. 11 Issue 23, p4715, 15p

Subject Terms: QUASI-Newton methods, DERIVATIVES (Mathematics), GEOMETRIC series, CONVEX functions, PROBLEM solving

A single timescale stochastic quasi-Newton method for stochastic optimization.

Authors: Wang, Peng Zhu, Detong

Source: International Journal of Computer Mathematics; Dec2023, Vol. 100 Issue 12, p2196-2216, 21p

Subject Terms: QUASI-Newton methods, HESSIAN matrices, SUPPORT vector machines, DERIVATIVES (Mathematics), SMOOTHNESS of functions

Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods.

Authors: Yousefi, Mahsa Martínez, Ángeles

Source: Algorithms; Oct2023, Vol. 16 Issue 10, p490, 26p

Subject Terms: ARTIFICIAL neural networks, QUASI-Newton methods, DEEP learning, PROBLEM solving

An Overview of Stochastic Quasi-Newton Methods for Large-Scale Machine Learning.

Authors: Guo, Tian-De Liu, Yan Han, Cong-Ying

Source: Journal of the Operations Research Society of China; Jun2023, Vol. 11 Issue 2, p245-275, 31p

Recent Advances in Stochastic Gradient Descent in Deep Learning.

Authors: Tian, Yingjie Zhang, Yuqi Zhang, Haibin

Source: Mathematics (2227-7390); Feb2023, Vol. 11 Issue 3, p682, 23p

Subject Terms: DEEP learning, NATURAL language processing, IMAGE processing, ARTIFICIAL intelligence, MACHINE learning

Joint rescaled asymmetric least squared nonparallel support vector machine with a stochastic quasi-Newton based algorithm.

Authors: Qi, Kai Yang, Hu

Source: Applied Intelligence; Sep2022, Vol. 52 Issue 12, p14387-14405, 19p

Subject Terms: SUPPORT vector machines, QUASI-Newton methods, ALGORITHMS, LEAST squares