English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Support vector machine (SVM) and neural networks (NN) have strongcomplementarity. SVM focuses on the inner operation among samples while NNfocuses on the operation among the features within samples. Thus, it ispromising and attractive to combine SVM and NN, as it may provide a morepowerful function than SVM or NN alone. However, current work on combining themlacks true integration. To address this, we propose a sample attention memorynetwork (SAMN) that effectively combines SVM and NN by incorporating sampleattention module, class prototypes, and memory block to NN. SVM can be viewedas a sample attention machine. It allows us to add a sample attention module toNN to implement the main function of SVM. Class prototypes are representativesof all classes, which can be viewed as alternatives to support vectors. Thememory block is used for the storage and update of class prototypes. Classprototypes and memory block effectively reduce the computational cost of sampleattention and make SAMN suitable for multi-classification tasks. Extensiveexperiments show that SAMN achieves better classification performance thansingle SVM or single NN with similar parameter sizes, as well as the previousbest model for combining SVM and NN. The sample attention mechanism is aflexible module that can be easily deepened and incorporated into neuralnetworks that require it.

SAMN: A Sample Attention Memory Network Combining SVM and NN in One Architecture