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

Inductive rule learning is arguably among the most traditional paradigms inmachine learning. Although we have seen considerable progress over the years inlearning rule-based theories, all state-of-the-art learners still learndescriptions that directly relate the input features to the target concept. Inthe simplest case, concept learning, this is a disjunctive normal form (DNF)description of the positive class. While it is clear that this is sufficientfrom a logical point of view because every logical expression can be reduced toan equivalent DNF expression, it could nevertheless be the case that morestructured representations, which form deep theories by forming intermediateconcepts, could be easier to learn, in very much the same way as deep neuralnetworks are able to outperform shallow networks, even though the latter arealso universal function approximators. In this paper, we empirically comparedeep and shallow rule learning with a uniform general algorithm, which relieson greedy mini-batch based optimization. Our experiments on both artificial andreal-world benchmark data indicate that deep rule networks outperform shallownetworks.

An Empirical Investigation into Deep and Shallow Rule Learning