From bacteria following simple chemical gradients to the brain distinguishing complex odour information, the ability to recognize molecular patterns is essential for biological organisms. This type of information-processing function has been implemen...
For robots equipped with an advanced computer vision-based system, object recognition has stringent real-time requirements. When the environment becomes complicated and keeps changing, existing works (e.g., template-matching strategy and machine-lear...
Recent advances in machine learning allow faster training, improved performance and increased interpretability of classification techniques. Consequently, their application in neuroscience is rapidly increasing. While classification approaches have p...
Humans recognize transformed images from a very small number of samples. Inspired by this idea, we evaluate a classification method that requires only one sample per class, while providing invariance to image transformations generated by a compact gr...
BACKGROUND: Spastic cerebral palsy (CP) is a leading cause of physical disability. Most people with spastic CP are born with it, but early diagnosis is challenging, and no current biomarker platform readily identifies affected individuals. The aim of...
Neural networks : the official journal of the International Neural Network Society
Jun 19, 2018
Distance metric learning has been a hot research spot recently due to its high effectiveness and efficiency in improving the performance of distance related methods, such as k nearest neighbors (kNN). Metric learning aims to learn a data-dependent me...
The intuitionistic fuzzy set, as a generation of fuzzy set, can express and process uncertainty much better. Distance measures between intuitionistic fuzzy sets are used to indicate the difference degree between the information carried by intuitionis...
Human activity recognition (HAR) is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applie...
Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feat...
Neural networks : the official journal of the International Neural Network Society
Jun 15, 2018
A novel sparsity-based stochastic pooling which integrates the advantages of max-pooling, average-pooling and stochastic pooling is introduced. The proposed pooling is designed to balance the advantages and disadvantages of max-pooling and average-po...
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