Suchergebnisse - Batch gradient learning algorithm

Deterministic convergence of complex mini-batch gradient learning algorithm for fully complex-valued neural networks

Autoren: Ying Zhang Huisheng Zhang Dongpo Xu et al.

Quelle: Neurocomputing. 407:185-193

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://www.sciencedirect.com/science/article/pii/S0925231220307591
https://dblp.uni-trier.de/db/journals/ijon/ijon407.html#ZhangZZX20
https://doi.org/10.1016/j.neucom.2020.04.114

Batch Gradient Learning Algorithm with Smoothing L1 Regularization for Feedforward Neural Networks

Autoren: Khidir Shaib Mohamed

Quelle: Computers, Vol 12, Iss 1, p 4 (2022)

Schlagwörter: convergence, batch gradient learning algorithm, feedforward neural networks, smoothing L1 regularization, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2073-431X/12/1/4; https://doaj.org/toc/2073-431X

Zugangs-URL: https://doaj.org/article/a653afa5d78b402097e464de3f641831

Convergence of batch gradient learning algorithm with smoothing L1/2 regularization for Sigma–Pi–Sigma neural networks

Autoren: Yan Liu Jing Wang Wei Wu et al.

Quelle: Neurocomputing. 151:333-341

Schlagwörter: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Quantum Algorithms for Solving Hard Constrained Optimization Problems

Autoren: Atchadé Adelomou, Parfait

Weitere Verfasser: University/Department: Universitat Ramon Llull. Departament d'Enginyeria

Thesis Advisors: Golobardes Ribé, Elisabet, Vilasís Cardona, Xavier

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Quantum Computing, Case Based Reasoning, Hard Constrained Optimization Problems, Variational Quantum Algorithms, Social Workers Problem, QUBO, Quantum Annealing, Tecnologies de la informació i de les comunicacions (TIC)

Time: 519.1

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/674103

Batch Gradient Learning Algorithm with Smoothing L 1 Regularization for Feedforward Neural Networks.

Autoren: Mohamed, Khidir Shaib

Quelle: Computers (2073-431X); Jan2023, Vol. 12 Issue 1, p4, 15p

Schlagwörter: FEEDFORWARD neural networks, MACHINE learning, ERROR functions, NP-hard problems

2D Prediction of the Nutritional Composition of Dishes from Food Images: Deep Learning Algorithm Selection and Data Curation Beyond the Nutrition5k Project.

Autoren: Bianco, Rachele Coluccia, Sergio Marinoni, Michela et al.

Quelle: Nutrients. Jul2025, Vol. 18 Issue 13, p2196. 23p.

Comparison of Tree‐Based Machine Learning Algorithms for Classification of Livestock Breeds Based On Post‐Thaw Spermatological Parameters.

Autoren: Özen, Doğukan Özen, Hülya Gül, Elif Bersu et al.

Quelle: Veterinary Medicine & Science. Sep2025, Vol. 11 Issue 5, p1-10. 10p.

Schlagworte: Livestock breeds, Machine learning, Semen analysis, CART algorithms, Classification, Cattle breeds

Rockburst Intensity Grade Prediction Model Based on Batch Gradient Descent and Multi-Scale Residual Deep Neural Network.

Autoren: Yu Zhang Mingkui Zhang Jitao Li et al.

Quelle: Computer Systems Science & Engineering. 2023, Vol. 47 Issue 2, p1987-2006. 20p.

Schlagwörter: Rock bursts, Artificial neural networks, Deep learning, Geotechnical engineering, Excavation (Civil engineering)

Optimization of digital landscape design system based on biomechanical simulation and machine learning algorithm in landscape architecture planning.

Autoren: Yang, Ping Wang, Yetong Li, Yixiong

Quelle: Molecular & Cellular Biomechanics; 2025, Vol. 22 Issue 5, p1-17, 17p

Schlagwörter: LANDSCAPE design, MACHINE learning, LANDSCAPE assessment, FINITE element method, RESOURCE allocation, DYNAMIC simulation

Convergence of Hyperbolic Neural Networks Under Riemannian Stochastic Gradient Descent

Autoren: Whiting, Wes Wang, Bao Xin, Jack

Quelle: Communications on Applied Mathematics and Computation. 6(2)

Schlagwörter: 46 Information and Computing Sciences (for-2020), 49 Mathematical Sciences (for-2020), 4611 Machine Learning (for-2020), Bioengineering (rcdc), Hyperbolic neural network, Riemannian gradient descent, Riemannian Adam (RAdam), Training convergence

Zugangs-URL: https://escholarship.org/uc/item/6w62m9nt

An Improved N-Step Value Gradient Learning Adaptive Dynamic Programming Algorithm for Online Learning.

Autoren: Al-Dabooni, Seaar Wunsch, Donald C.

Quelle: IEEE Transactions on Neural Networks & Learning Systems; Apr2020, Vol. 31 Issue 4, p1155-1169, 15p

Schlagwörter: ONLINE algorithms, ONLINE education, HEURISTIC programming, MACHINE learning, ACTING education

Path planning of mobile robot based on improved double deep Q-network algorithm.

Autoren: Wang, Zhenggang Song, Shuhong Cheng, Shenghui

Quelle: Frontiers in Neurorobotics; 2025, p1-17, 17p

Schlagwörter: DEEP reinforcement learning, MACHINE learning, LONG short-term memory, ROBOTIC path planning, MOBILE robots, REINFORCEMENT learning

Deep-learning-based buffalo identification through muzzle pattern images.

Autoren: Ermetin, Orhan Örnek, Humar Kahramanlı

Quelle: Archives Animal Breeding / Archiv Tierzucht. 2025, Vol. 68 Issue 3, p473-484. 12p.

Schlagworte: Artificial intelligence, Water buffalo, Artificial neural networks, Biometric identification, Livestock, Convolutional neural networks, Deep learning, Identification of animals

Augmenting interpretation of vaginoscopy observations in cycling bitches with deep learning model.

Autoren: K. Rajan, Bindhu G., Venugopal Harshan M., Hiron et al.

Quelle: BMC Veterinary Research. 9/9/2024, Vol. 20 Issue 1, p1-13. 13p.

Schlagworte: Convolutional neural networks, Deep learning, Support vector machines, Anestrus, K-nearest neighbor classification, Estrus

METHOD, PROGRAM, AND DEVICE FOR TRAINING ARTIFICIAL NEURAL NETWORK BASED ON ADAPTIVE STOCHASTIC GRADIENT DESCENT IN MEMORY-BASED CONTINUAL LEARNING SITUATION

Deep learning framework for vertebral heart size and cardiothoracic ratio estimation in dogs and cats using thoracic radiographs.

Autoren: Mekonnen, Habtamu Tilahun Puig, Núria Elson, Alejandro et al.

Quelle: Frontiers in Veterinary Science. 2025, p1-16. 16p.

Impact of multi‐output and stacking methods on feed efficiency prediction from genotype using machine learning algorithms.

Autoren: Mora, Mónica González, Pablo Quevedo, José Ramón et al.

Quelle: Journal of Animal Breeding & Genetics. Nov2023, Vol. 140 Issue 6, p638-652. 15p.

Schlagworte: Machine learning, Swine breeding, Independent variables, Genotypes, Livestock breeding, Feature selection

Towards standardizing mitral transcatheter edge-to-edge repair with deep-learning algorithm: a comprehensive multi-model strategy.

Autoren: Corona, Silvia Godefroy, Théo Tastet, Olivier et al.

Quelle: Frontiers in Network Physiology; 2025, p1-17, 17p

K-cql: an arterial blood gas analysis-based deep offline reinforcement learning algorithm for mechanical ventilation treatment.

Autoren: Xi, Jiaying Dong, Shaojie Zhou, Haoquan et al.

Quelle: Health Information Science & Systems; 8/6/2025, Vol. 13 Issue 1, p1-12, 12p

Integrating Physics-Informed Neural Network Model for State-of-Charge and Internal States Estimation in the Lead-Acid Batteries.

Autoren: Pathak, Akanksha Nandanwar, Mahendra N.

Quelle: Journal of The Electrochemical Society. Nov2025, Vol. 172 Issue 11, p1-13. 13p.

Schlagwörter: Lead-acid batteries, Battery management systems, Machine learning, Empirical research, Electric charge, Electrochemical experiments