This paper presents an empirical study of a formative neural network-based assessment approach by using mobile technology to provide pharmacy students with intelligent diagnostic feedback. An unsupervised learning algorithm was integrated with an aud...
Computational intelligence and neuroscience
Jan 14, 2016
An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the glo...
Computational intelligence and neuroscience
Jan 14, 2016
Color quantization is an essential technique in color image processing, which has been continuously researched. It is often used, in particular, as preprocessing for many applications. Self-Organizing Map (SOM) color quantization is one of the most e...
Computational intelligence and neuroscience
Jan 4, 2016
In recent years, imbalanced learning problem has attracted more and more attentions from both academia and industry, and the problem is concerned with the performance of learning algorithms in the presence of data with severe class distribution skews...
Computational intelligence and neuroscience
Dec 30, 2015
An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy ba...
Computational intelligence and neuroscience
Dec 29, 2015
The most important asset of semisupervised classification methods is the use of available unlabeled data combined with a clearly smaller set of labeled examples, so as to increase the classification accuracy compared with the default procedure of sup...
Computational intelligence and neuroscience
Dec 27, 2015
Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior o...
Why do some neurons in hippocampus and cortex respond to information in a highly selective manner? It has been hypothesized that neurons in hippocampus encode information in a highly selective manner in order to support fast learning without catastro...
Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)-based a...
The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be ...
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