Fine-Grained Image Classification (FGIC) aims to classify images into specific subordinate classes of a superclass. Due insufficient training data and confusing samples, FGIC may produce uncertain classification results that are untrusted for applications. In fact, can be viewed as hierarchical process the multilayer information facilitates reduce uncertainty improve reliability FGIC. this pape...

Feature selection is a widespread preprocessing step in the data mining field. One of its purposes to reduce number original dataset features improve predictive model’s performance. Despite benefits feature for classification task, best our knowledge, few studies literature address hierarchical context. This paper proposes novel method based on general variable neighborhood search metaheuristic...

A well-designed loss function can effectively improve the characterization ability of network features without increasing amount calculation in model inference stage, and has become focus attention recent research. Given that existing lightweight adds a to last layer, which severely attenuates gradient during backpropagation process, we propose hierarchical polynomial kernel prototype this stud...

Document classification is a fundamental problem in natural language processing. Deep learning has demonstrated great success this task. However, most existing models do not involve the sentence structure as text semantic feature architecture and pay less attention to contexting importance of words sentences. In paper, we present new model based on sparse recurrent neural network self-attention...