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

A diagnostic policy specifies what test to perform next, based on the resultsof previous tests, and when to stop and make a diagnosis. Cost-sensitivediagnostic policies perform tradeoffs between (a) the cost of tests and (b) thecost of misdiagnoses. An optimal diagnostic policy minimizes the expected totalcost. We formalize this diagnosis process as a Markov Decision Process (MDP).We investigate two types of algorithms for solving this MDP: systematic searchbased on AO* algorithm and greedy search (particularly the Value of Informationmethod). We investigate the issue of learning the MDP probabilities fromexamples, but only as they are relevant to the search for good policies. We donot learn nor assume a Bayesian network for the diagnosis process. Regularizersare developed to control overfitting and speed up the search. This research isthe first that integrates overfitting prevention into systematic search. Thepaper has two contributions: it discusses the factors that make systematicsearch feasible for diagnosis, and it shows experimentally, on benchmark datasets, that systematic search methods produce better diagnostic policies thangreedy methods.

Learning Diagnostic Policies from Examples by Systematic Search