scholarly journals Linear programming applied to separation detection in polytomous logistic regression

2021 ◽  
Author(s):  
Inácio Andruski Guimarães ◽  
Thiago Schinda Bubniak
Keyword(s):  
Logistic Regression ◽  
Linear Programming ◽  
Polytomous Logistic Regression

scholarly journals CLASSIFICATION OF BIPOLAR DISORDER, MAJOR DEPRESSIVE DISORDER, AND HEALTHY STATE USING VOICE

2018 ◽  
Vol 11 (15) ◽  
pp. 89 ◽  
Author(s):  
Masakazu Higuchi ◽  
Shinichi Tokuno ◽  
Mitsuteru Nakamura ◽  
Shuji Shinohara ◽  
Shunji Mitsuyoshi ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Logistic Regression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mental Disease ◽  
Healthy Individuals ◽  
Major Depressive ◽  
Healthy State ◽  
Polytomous Logistic Regression

Objective: In this study, we propose a voice index to identify healthy individuals, patients with bipolar disorder, and patients with major depressive disorder using polytomous logistic regression analysis.Methods: Voice features were extracted from voices of healthy individuals and patients with mental disease. Polytomous logistic regression analysis was performed for some voice features.Results: With the prediction model obtained using the analysis, we identified subject groups and were able to classify subjects into three groups with 90.79% accuracy.Conclusion: These results show that the proposed index may be used as a new evaluation index to identify depression.

scholarly journals Machine Meets Man: Evaluating the Psychological Reality of Corpus-based Probabilistic Models

2016 ◽  
Vol 27 (1) ◽  
pp. 1-33 ◽  
Author(s):  
Dagmar Divjak ◽  
Ewa Dąbrowska ◽  
Antti Arppe
Keyword(s):  
Logistic Regression ◽  
Logistic Regression Model ◽  
Probabilistic Models ◽  
Native Speakers ◽  
Linguistic Knowledge ◽  
Sentential Context ◽  
Linguistic Methodology ◽  
Linguistic Convention ◽  
Polytomous Logistic Regression ◽  
Selection Of

AbstractLinguistic convention typically allows speakers several options. Evidence is accumulating that the various options are preferred in different contexts, yet the criteria governing the selection of the appropriate form are often far from obvious. Most researchers who attempt to discover the factors determining a preference rely on the linguistic analysis and statistical modeling of data extracted from large corpora. In this paper, we address the question of how to evaluate such models and explicitly compare the performance of a statistical model derived from a corpus with that of native speakers in selecting one of six Russian TRY verbs. Building on earlier work we trained a polytomous logistic regression model to predict verb choice given the sentential context. We compare the predictions the model makes for 60 unseen sentences to the choices adult native speakers make in those same sentences. We then look in more detail at the interplay of the contextual properties and model computationally how individual differences in assessing the importance of contextual properties may impact the linguistic knowledge of native speakers. Finally, we compare the probability the model assigns to encountering each of the six verbs in the 60 test sentences to the acceptability ratings the adult native speakers give to those sentences. We discuss the implications of our findings for both usage-based theory and empirical linguistic methodology.

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