Nettsider med emneord «Machine Learning» - Side 5

Publisert 6. nov. 2017 11:32
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Publisert 5. okt. 2017 09:03

Schneider Electric Norway AS has proposed master projects that will be co-supervised by them and will involve real and challenging ICT problems in their organisation. Part of the work will be done at their offices at Ryen. 

Publisert 4. okt. 2017 14:53
Publisert 28. sep. 2017 21:05
Publisert 19. juni 2017 15:22
Publisert 12. juni 2017 23:12

The SANT project aims to create training data and machine-learned models for Sentiment Analysis for Norwegian Text. While coordinated by the Language Technology Group at IFI/UiO, collaborating partners include NRK, Schibsted and Aller Media.

Publisert 2. feb. 2017 12:51
Publisert 2. feb. 2017 10:58

Cardiac related disease is the number one cause of death in the Western world, including Norway. Echocardiography is the most important imaging tool for the cardiologist to assess cardiac function. An echo examination of the heart is real time, cost effective and can be performed without discomfort to the patient and without harmful radiation. These are great advantages compared to other medical imaging modalities.

Publisert 21. des. 2016 15:29
Publisert 9. des. 2016 10:00
Publisert 8. nov. 2016 13:15

In this ongoing cross-disciplinary collaboration, researchers in Language Technology (LT) and Political Science (PS) are applying supervised and unsupervised machine learning methods to data from the Norwegian parliament in order to gather knowledge spanning across different dimensions.

Publisert 12. okt. 2016 09:51
Publisert 20. juni 2016 12:10

Obstructive sleep apnea (OSA) is a common but severely under-diagnosed sleep disorder that affects the natural breathing cycle during sleep with the periods of reduced respiration or no airflow at all. It is our long-term goal to increase the percentage of diagnosed OSA cases, reduce the time to diagnosis, and support long term monitoring of patients with user friendly and cost-efficient tools for sleep analysis at home. Core elements are mobile computing platforms (e.g., smartphones), consumer electronics sensors, and machine learning for OSA detection.

Publisert 24. okt. 2015 00:15
Publisert 14. okt. 2015 18:11
Publisert 22. sep. 2015 10:18
Publisert 1. okt. 2013 08:28
Publisert 5. juni 2013 12:36
Publisert 19. des. 2012 10:56
Publisert 2. okt. 2012 10:40


The use of lexical semantic information for the task of syntactic parsing has seen varied success. Recently, however, the use of lexical semantic clusters derived from large corpora has been shown to improve parsing performance. It is still unclear, however, how different properties of these clusters affect results. This project aims to investigate the use of different types of clusters during syntactic parsing. 

More precisely the idea is to use word clusters as a source for features in a statistical disambiguation model for a dependency parser. Generally, the clusters will group together words with similar distributional properties. The exact nature of these similarity relations, however, will vary depending on the types of context features that are used when performing the clustering. For this project we will basically be doing an extrinsic form of cluster evaluation then; investigating how different clustering parameters in turn affect the performance of a statistical parser.