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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Creating computational models from large and growing datasets is an important issue in current machine learning research, because most modelling approaches can require prohibitive computational resources. This work presents the use of incremental learning algorithms within the framework of an incremental modelling approach. In particular, it presents the GNG-m algorithm, an adaptation of the Growing Neural Gas algorithm (GNG), capable of circumventing the problem of catastrophic forgetting when modelling large datasets in a sequential manner. We illustrate this by comparing the performance of GNG-m with that of the original GNG algorithm, on a vector quantization task. Last but not least, we present the use of GNG-m in an incremental modelling task using a real-world database of temperature, coming from a geographic information system (GIS). The dataset of more than one million multidimensional observations is split in seven parts and then reduced by vector quantization to a codebook of only thousands of prototypes.

USING THE GNG-M ALGORITHM TO DEAL WITH THE PROBLEM OF CATASTROPHIC FORGETTING IN INCREMENTAL MODELLING