https://ogma.newcastle.edu.au/vital/access/manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:20659 Wed 11 Apr 2018 14:30:55 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:18713 Wed 11 Apr 2018 12:06:13 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:25313 Wed 11 Apr 2018 09:18:04 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:45579 Wed 02 Nov 2022 08:23:58 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:54429 Tue 27 Feb 2024 14:00:08 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:43528 Tue 20 Aug 2024 12:34:13 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:32234 Tue 15 May 2018 14:22:14 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:55507 Tue 04 Jun 2024 20:54:48 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:48535 Diagnostic and Statistical Manuals of Mental Disorders “DSM-IV” and the most recent addition, DSM-5 allow us to introspect, using the solution provided by modern algorithms, if there exists a consensus between the clusters obtained via a data-driven approach, with the current classifications. In the case of mental disorders, the availability of a follow-up consensus collection (e.g. in this case the DSM-5), potentially allows investigating if the classification of disorders has moved closer (or away) to what a data-driven analytic approach would have unveiled by objectively inferring it from the data of DSM-IV. In this contribution, we present a new type of mathematical approach based on a global cohesion score which we introduce for the first time for the identification of communities of symptoms. Different from other approaches, this combinatorial optimization method is based on the identification of “triangles” in the network; these triads are the building block of feedback loops that can exist between groups of symptoms. We used a memetic algorithm to obtain a collection of highly connected-cohesive sets of symptoms and we compare the resulting community structure with the classification of disorders present in the DSM-IV.]]> Thu 22 Aug 2024 18:06:57 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:50785 Sat 05 Aug 2023 11:23:08 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:27427 Mon 23 Sep 2019 13:35:41 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:38588 th century for which measurements were tabulated, and a governing functional relationship was postulated. Using leave-one-out cross-validation, in training our method ranks first in 350 out of the 352 datasets. Only six machine learning algorithms ranked first in at least one of the 352 datasets on testing; our approach ranked first 192 times, i.e. more all of the other algorithms combined. The results favourably speak about the robustness of our methodology. We conclude that the use of analytic continued fractions in regression deserves further study and we also advocate that Schaffer's data collection should also be included in the repertoire of datasets to test the performance of machine learning and regression algorithms.]]> Mon 20 Nov 2023 14:40:26 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:46269 Mon 14 Nov 2022 14:59:50 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:28905 Mon 06 Aug 2018 16:27:50 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:42849 Mon 05 Sep 2022 15:26:27 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:50777 Mon 05 Aug 2024 12:14:36 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:24365 Fri 23 Aug 2024 08:42:54 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:55584 Fri 07 Jun 2024 12:01:40 AEST ]]> https://ogma.newcastle.edu.au/vital/access/manager/Repository/uon:42778 Fri 02 Sep 2022 11:42:18 AEST ]]>