Global optimization issues in deep network regression: an overview
From MaRDI portal
Publication:2633536
DOI10.1007/s10898-018-0701-7zbMath1421.90154OpenAlexW2891899765WikidataQ129326537 ScholiaQ129326537MaRDI QIDQ2633536
Publication date: 9 May 2019
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10898-018-0701-7
global optimizationsupervised learninghybrid algorithmsfeedforward neural networksdeep networksweights optimization
Related Items
Mathematical optimization in classification and regression trees, Block layer decomposition schemes for training deep neural networks
Uses Software
Cites Work
- A nested heuristic for parameter tuning in support vector machines
- Supervised classification and mathematical optimization
- Application of global optimization methods to model and feature selection
- The dropout learning algorithm
- A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training
- Trends in extreme learning machines: a review
- A filled function method for finding a global minimizer of a function of several variables
- Towards ``Ideal multistart. A stochastic approach for locating the minima of a continuous function inside a bounded domain
- Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing
- Training multilayer neural networks using fast global learning algorithm -- least-squares and penalized optimization methods
- Terminal repeller unconstrained subenergy tunneling (TRUST) for fast global optimization
- Unsupervised and supervised data classification via nonsmooth and global optimization (with comments and rejoinder)
- Fast training of support vector machines with Gaussian kernel
- Optimization approaches to supervised classification
- A distribution-free theory of nonparametric regression
- A hybrid projection-based and radial basis function architecture: Initial values and global optimisation
- Nonmonotone globalization techniques for the Barzilai-Borwein gradient method
- Learning in multilayer perceptrons using global optimization strategies.
- Complex-valued autoencoders
- Semiparametric regression during 2003--2007
- Editorial: Randomized algorithms for training neural networks
- Convergent Decomposition Techniques for Training RBF Neural Networks
- Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
- TRUST: A Deterministic Algorithm for Global Optimization
- Flat Minima
- Classification and Regression via Integer Optimization
- Globally convergent block-coordinate techniques for unconstrained optimization
- Training a Single Sigmoidal Neuron Is Hard
- Semiparametric Regression
- Gradient Convergence in Gradient methods with Errors
- Optimization Methods for Large-Scale Machine Learning
- Improved Learning of Neural Nets Through Global Search
- Some methods of speeding up the convergence of iteration methods
- A Stochastic Approximation Method
- A new filled function method for unconstrained global optimization
- Nonlinear optimization and support vector machines
- Optimal classification trees
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item