Prototype Generation (PG) methods are typically considered for improving the efficiency of
the k-Nearest Neighbour (k NN) classifier when tackling high-size corpora. Such …

A very common practice to speed up instance based classifiers is to reduce the size of their
training set, that is, replace it by a condensing set, hoping that their accuracy will not worsen …

Online cross-modal hashing has received increasing attention due to its efficiency and
effectiveness in handling cross-modal streaming data retrieval. Despite the promising …

In fields like security risk analysis, Fast Moving Consumer Goods, Internet of Things, or the
banking sector, it is necessary to deal with large datasets containing a great list of variables …

Reducing the size of the training set, which involves replacing it with a condensed set, is a
widely adopted practice to enhance the efficiency of instance-based classifiers while trying …

In the context of discrete choice modeling, the extraction of potential behavioral insights from
large datasets is often limited by the poor scalability of maximum likelihood estimation. This …

Prototype Generation (PG) methods seek to improve the efficiency of the k-Nearest Neighbor
(k NN) classifier by obtaining a reduced version of a given reference dataset following …