I'm interested in developing new solutions for pattern recognition, data mining, computer vision and image processing.
Jeremiah D. Deng
Associate Professor at University of Otago
Summary
Published articles Show More
Benchmarking Stochastic Algorithms for Global Optimization Problems by Visualizing Confidence Intervals
Published in IEEE Transactions on Cybernetics
The popular performance profiles and data profiles for benchmarking deterministic optimization algorithms are extended to benchmark stochastic algorithms for global optimization problems. A general confidence interval is employed to replace the significance test, which is popular in traditional benchmarking methods but suffering more and more criti...
Segment-Based Predominant Learning Swarm Optimizer for Large-Scale Optimization
Published in IEEE Transactions on Cybernetics
Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is ...
A streaming ensemble classifier with multi-class imbalance learning for activity recognition
Published in 2017 International Joint Conference on Neural Networks (IJCNN)
Stream multi-class imbalance learning in smart home applications is an evolving learning area that incorporates the challenges of both multi-class imbalance and stream learning. Moreover, another argument in the learning from the imbalanced multi-class distributions that cause misleading classification outcomes, is the imbalanced ratio in a sensor ...
Preprints Show More
Image segmentation with superpixel-based covariance descriptors in low-rank representation [arXiv]
This paper investigates the problem of image segmentation using superpixels. We propose two approaches to enhance the discriminative ability of the superpixel's covariance descriptors. In the first one, we employ the Log-Euclidean distance as the metric on the covariance manifolds, and then use the RBF kernel to measure the similarities between cov...
Experience
Associate Professor Since 2014
University of Otago
Awards
Best Paper, ICONIP 2017 November 2017
24th International Conference on Neural Information Processing, ICONIP'2017, November Guangzhou
Top 10% Paper, ICIP'13 2013
IEEE International Conference on Image Processing, ICIP'13, Paris
