A self-adaptive gradient descent search algorithm for fully-connected neural networks
出版年份 2022 全文链接
标题
A self-adaptive gradient descent search algorithm for fully-connected neural networks
作者
关键词
Neural network, Population-based gradient descent, Self-adaptive mechanism, Parameters search
出版物
NEUROCOMPUTING
Volume 478, Issue -, Pages 70-80
出版商
Elsevier BV
发表日期
2022-01-12
DOI
10.1016/j.neucom.2022.01.001
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification
- (2020) Yanan Sun et al. IEEE Transactions on Cybernetics
- Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification
- (2019) Yu Xue et al. ACM Transactions on Knowledge Discovery from Data
- Evolving Deep Convolutional Neural Networks for Image Classification
- (2019) Yanan Sun et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- Completely Automated CNN Architecture Design Based on Blocks
- (2019) Yanan Sun et al. IEEE Transactions on Neural Networks and Learning Systems
- Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations
- (2018) Yanan Sun et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- A multiobjective optimization-based sparse extreme learning machine algorithm
- (2018) Yu Wu et al. NEUROCOMPUTING
- A Self-Adaptive Fireworks Algorithm for Classification Problems
- (2018) Yu Xue et al. IEEE Access
- A hybrid self-adaptive sine cosine algorithm with opposition based learning
- (2018) Shubham Gupta et al. EXPERT SYSTEMS WITH APPLICATIONS
- A self-adaptive artificial bee colony algorithm based on global best for global optimization
- (2017) Yu Xue et al. SOFT COMPUTING
- Neural network classifier optimization using Differential Evolution with Global Information and Back Propagation algorithm for clinical datasets
- (2016) N. Leema et al. APPLIED SOFT COMPUTING
- Configuring two-algorithm-based evolutionary approach for solving dynamic economic dispatch problems
- (2016) Forhad Zaman et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- How effective is the Grey Wolf optimizer in training multi-layer perceptrons
- (2015) Seyedali Mirjalili APPLIED INTELLIGENCE
- A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems
- (2014) Wenchao Yi et al. APPLIED INTELLIGENCE
- Audio-visual speech recognition using deep learning
- (2014) Kuniaki Noda et al. APPLIED INTELLIGENCE
- Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]
- (2014) Erik Cambria et al. IEEE Computational Intelligence Magazine
- Detecting changing emotions in human speech by machine and humans
- (2013) C. Natalie van der Wal et al. APPLIED INTELLIGENCE
- Application of artificial neural network–genetic algorithm (ANN–GA) to correlation of density in nanofluids
- (2012) Hajir Karimi et al. FLUID PHASE EQUILIBRIA
- Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach
- (2012) Bing Xue et al. IEEE Transactions on Cybernetics
- An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization
- (2011) S. M. Islam et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
- A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN
- (2009) Satchidananda Dehuri et al. NEURAL COMPUTING & APPLICATIONS
- Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
- (2008) A.K. Qin et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks
- (2008) De-Shuang Huang et al. IEEE TRANSACTIONS ON NEURAL NETWORKS
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