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

Global optimization of decision trees has shown to be promising in terms ofaccuracy, size, and consequently human comprehensibility. However, many of themethods used rely on general-purpose solvers for which scalability remains anissue. Dynamic programming methods have been shown to scale much better becausethey exploit the tree structure by solving subtrees as independent subproblems.However, this only works when an objective can be optimized separately forsubtrees. We explore this relationship in detail and show the necessary andsufficient conditions for such separability and generalize previous dynamicprogramming approaches into a framework that can optimize any combination ofseparable objectives and constraints. Experiments on five application domainsshow the general applicability of this framework, while outperforming thescalability of general-purpose solvers by a large margin.

Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming