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Found 7 papers, 1 papers with code
Training Artificial Neural Networks by Coordinate Search Algorithm
Finding the optimal values for weights of ANNs is a large-scale optimization problem.
Compact NSGA-II for Multi-objective Feature Selection
In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features.
Multi-objective Binary Coordinate Search for Feature Selection
For this purpose, we have proposed the binary multi-objective coordinate search (MOCS) algorithm to solve large-scale feature selection problems.
Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology
By forcing the model to learn the ranking of matched outputs, the representation learning is customized toward image search instead of learning a class label.
Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images
Coarse multi-objective feature selection uses the reduced search space strategy guided by the classification accuracy and the number of features.
Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework
The generated pseudo labels of our proposed framework can be fed to various DNNs to improve their generalization capacity.
Ranked #1 on
Semi-Supervised Image Classification
on Salinas
(using extra training data)
Image-Based Benchmarking and Visualization for Large-Scale Global Optimization
In the context of optimization, visualization techniques can be useful for understanding the behaviour of optimization algorithms and can even provide a means to facilitate human interaction with an optimizer.
