scholarly journals Automatic Detection of Compensatory Movement Patterns by a Pressure Distribution Mattress Using Machine Learning Methods: A Pilot Study

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80300-80309 ◽  
Author(s):  
Siqi Cai ◽  
Guofeng Li ◽  
Shuangyuan Huang ◽  
Haiqing Zheng ◽  
Longhan Xie
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Pressure Distribution ◽  
Movement Patterns ◽  
Automatic Detection ◽  
Learning Methods ◽  
Compensatory Movement ◽  
Machine Learning Methods

scholarly journals Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods

EBioMedicine ◽  
2018 ◽  
Vol 27 ◽  
pp. 94-102 ◽  
Author(s):  
Xiangyi Kong ◽  
Shun Gong ◽  
Lijuan Su ◽  
Newton Howard ◽  
Yanguo Kong
Keyword(s):  
Machine Learning ◽  
Automatic Detection ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals A pilot study using machine learning methods about factors influencing prognosis of dental implants

2018 ◽  
Vol 10 (6) ◽  
pp. 395 ◽  
Author(s):  
Seung-Ryong Ha ◽  
Hyun Sung Park ◽  
Eung-Hee Kim ◽  
Hong-Ki Kim ◽  
Jin-Yong Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Dental Implants ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Factors Influencing

scholarly journals Classifying Non-Sentential Utterances in Dialogue: A Machine Learning Approach

2007 ◽  
Vol 33 (3) ◽  
pp. 397-427 ◽  
Author(s):  
Raquel Fernández ◽  
Jonathan Ginzburg ◽  
Shalom Lappin
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Full Range ◽  
Learning Approach ◽  
Learning Methods ◽  
Fine Grained ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
The Right

In this article we use well-known machine learning methods to tackle a novel task, namely the classification of non-sentential utterances (NSUs) in dialogue. We introduce a fine-grained taxonomy of NSU classes based on corpus work, and then report on the results of several machine learning experiments. First, we present a pilot study focused on one of the NSU classes in the taxonomy—bare wh-phrases or “sluices”—and explore the task of disambiguating between the different readings that sluices can convey. We then extend the approach to classify the full range of NSU classes, obtaining results of around an 87% weighted F-score. Thus our experiments show that, for the taxonomy adopted, the task of identifying the right NSU class can be successfully learned, and hence provide a very encouraging basis for the more general enterprise of fully processing NSUs.

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