Search Results for author: Shengxiang Yang

Found 11 papers, 4 papers with code

Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey

no code implementations1 Mar 2022 Saul Calderon-Ramirez, Shengxiang Yang, David Elizondo

In a semi-supervised setting, unlabelled data is used to improve the levels of accuracy and generalization of a model with small labelled datasets.

Autonomous Driving Image Classification

A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica

no code implementations24 Jul 2021 Saul Calderon-Ramirez, Diego Murillo-Hernandez, Kevin Rojas-Salazar, David Elizondo, Shengxiang Yang, Miguel Molina-Cabello

The use of two popular and publicly available datasets (INbreast and CBIS-DDSM) as source data, to train and test the models on the novel target dataset, is evaluated.

Transfer Learning

Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark

1 code implementation23 Jul 2021 Mohammad Nabi Omidvar, Danial Yazdani, Juergen Branke, XiaoDong Li, Shengxiang Yang, Xin Yao

This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems.

AREA: Adaptive Reference-set Based Evolutionary Algorithm for Multiobjective Optimisation

no code implementations15 Oct 2019 Shouyong Jiang, Hongru Li, Jinglei Guo, Mingjun Zhong, Shengxiang Yang, Marcus Kaiser, Natalio Krasnogor

The proposed framework is combined with new strategies, such as reference adaptation and adaptive local mating, to solve different types of problems.

A Scalable Test Suite for Continuous Dynamic Multiobjective Optimisation

no code implementations6 Mar 2019 Shouyong Jiang, Marcus Kaiser, Shengxiang Yang, Stefanos Kollias, Natalio Krasnogor

It is demonstrated with empirical studies that the proposed test suite is more challenging to the dynamic multiobjective optimisation algorithms found in the literature.

An Adaptive Framework to Tune the Coordinate Systems in Evolutionary Algorithms

no code implementations18 Mar 2017 Zhi-Zhong Liu, Yong Wang, Shengxiang Yang, Ke Tang

In the evolutionary computation research community, the performance of most evolutionary algorithms (EAs) depends strongly on their implemented coordinate system.

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