Foreign Exchange Prediction Using Machine Learning Approach: A Pilot Study

2021 ◽  
Author(s):  
Sudimanto ◽  
Yaya Heryadi ◽  
Lukas ◽  
Antoni Wibowo
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Foreign Exchange ◽  
Learning Approach ◽  
Machine Learning Approach

Predicting functional impairment in bipolar disorder: a pilot study with a machine learning approach

2017 ◽  
Vol 27 ◽  
pp. S822-S823
Author(s):  
J.V. Pinto ◽  
D. Librenza-Garcia ◽  
D.P. Baldez ◽  
A.R. Rosa ◽  
F. Kapczinski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Pilot Study ◽  
Functional Impairment ◽  
Learning Approach ◽  
Machine Learning Approach

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.

scholarly journals Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach

2016 ◽  
Vol 193 ◽  
pp. 109-116 ◽  
Author(s):  
Ives Cavalcante Passos ◽  
Benson Mwangi ◽  
Bo Cao ◽  
Jane E. Hamilton ◽  
Mon-Ju Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Mood Disorders ◽  
Learning Approach ◽  
Machine Learning Approach
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