Neural networks : the official journal of the International Neural Network Society
Feb 24, 2020
Although our brain and deep neural networks (DNNs) can perform high-level sensory-perception tasks, such as image or speech recognition, the inner mechanism of these hierarchical information-processing systems is poorly understood in both neuroscienc...
Kernel extreme learning machine (KELM) introduces kernel leaning into extreme learning machine (ELM) in order to improve the generalization ability and stability. But the Penalty parameter in KELM is randomly set and it has a strong impact on the per...
Computational intelligence and neuroscience
Nov 1, 2018
Multiple kernel learning (MKL) as an approach to automated kernel selection plays an important role in machine learning. Some learning theories have been built to analyze the generalization of multiple kernel learning. However, less work has been stu...
Transposition is a tendency for organisms to generalize relationships between stimuli in situations where training does not objectively reward relationships over absolute, static associations. Transposition has most commonly been explained as either ...
Visual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categ...
Stacked generalization is an ensemble method that allows researchers to combine several different prediction algorithms into one. Since its introduction in the early 1990s, the method has evolved several times into a host of methods among which is th...
IEEE transactions on neural networks and learning systems
Jan 17, 2018
Online learning has been successfully applied in various machine learning problems. Conventional analysis of online learning achieves a sharp generalization bound with a strongly convex assumption. In this paper, we study the generalization ability o...
Previous research suggests that the effectiveness of robotic training depends on the motor task to be learned. However, it is still an open question which specific task's characteristics influence the efficacy of error-modulating training strategies....
Computational intelligence and neuroscience
Dec 27, 2015
Domain adaptation has received much attention as a major form of transfer learning. One issue that should be considered in domain adaptation is the gap between source domain and target domain. In order to improve the generalization ability of domain ...
A central question in language acquisition is how children build linguistic representations that allow them to generalize verbs from one construction to another (e.g., The boy gave a present to the girl → The boy gave the girl a present), whilst appr...
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