Deep Neural Network (DNN), as a deep architectures, has shown excellent performance in classification tasks. However, when the data has different distributions or contains some latent non-observed factors, it is difficult for DNN to train a single mo...
Fine-grained categorization is one of the most challenging problems in machine vision. Recently, the presented methods have been based on convolutional neural networks, increasing the accuracy of classification very significantly. Inspired by these m...
In this article, the proposed method develops a big data classification model with the aid of intelligent techniques. Here, the Parallel Pool Map reduce Framework is used for handling big data. The model involves three main phases, namely (1) feature...
Perspectives on psychological science : a journal of the Association for Psychological Science
32013765
My goal in searching for the big pictures is to discover novel ways of organizing information in psychology that will have both theoretical and practical significance. The first section lists my reasons for writing each of five articles. The second s...
Neural networks : the official journal of the International Neural Network Society
32087390
Collaborative representation-based classification (CRC) is a famous representation-based classification method in pattern recognition. Recently, many variants of CRC have been designed for many classification tasks with the good classification perfor...
Journal of the American Medical Informatics Association : JAMIA
32374408
OBJECTIVE: Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We dev...
IEEE transactions on neural networks and learning systems
32078563
Recently, applications of complex-valued neural networks (CVNNs) to real-valued classification problems have attracted significant attention. However, most existing CVNNs are black-box models with poor explanation performance. This study extends the ...
IEEE transactions on neural networks and learning systems
32203035
Learning vector quantization (LVQ) is a simple and efficient classification method, enjoying great popularity. However, in many classification scenarios, such as electroencephalogram (EEG) classification, the input features are represented by symmetr...
IEEE transactions on neural networks and learning systems
32175877
The state-of-the-art multitask multiview (MTMV) learning tackles a scenario where multiple tasks are related to each other via multiple shared feature views. However, in many real-world scenarios where a sequence of the multiview task comes, the high...
IEEE transactions on neural networks and learning systems
32167916
Despite their accuracy, neural network-based classifiers are still prone to manipulation through adversarial perturbations. These perturbations are designed to be misclassified by the neural network while being perceptually identical to some valid in...