What is hierarchical neural network?

What is hierarchical neural network?

Hierarchical neural networks consist of multiple neural networks concreted in a form of an acyclic graph. The networks within the graph can be single neurons or complexer neural architectures such as multilayer perceptrons or radial basis function networks.

What is hierarchical CNN?

Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning. The proposed hierarchical model, when compared against fine-tuning a deep network, achieves significant reduction of training effort, while maintaining competitive accuracy on CIFAR-10 and CIFAR-100.

How is a hierarchical attention network used in text classification?

First, the network considers the hierarchical structure of documents by constructing a document representation by building representations of sentences and then aggregating those into a document representation.

What is hierarchical multi label classification with local multi-layer perceptron?

Hierarchical Multi-label Classification with Local Multi-Layer Perceptron (HMC-LMLP), is a local-based HMC method that associates one Multi-Layer Perceptron (MLP) to each classification hierarchical level. It is a method designed to be used in tree structured hierarchies.

How is hierarchical structure used in text classification?

Several approaches successfully covered the hierarchical structure of documents (e.g. Zhou et al., 2015) and computed importance weights. Still, contexts of words and sentences, including the changing meanings in different documents, are new to text classification tasks and find a first solution with HAN.

How is lcpn and LCL used in hierarchical classification?

The LCPN strategy trains, for each internal class, a multi-class classifier to distinguish between its subclasses [17], and the LCL strategy trains one multi-class classifier for each hierarchical level, where each classifier is responsible for the prediction in its associated level [11].