Typological variation of kinship terminologies is a function of strict ranking of constraints on nested binary classification trees

2010 ◽  
Vol 33 (5) ◽  
pp. 395-397
Author(s):  
Paul Miers
Keyword(s):  
Binary Classification ◽  
Classification Trees ◽  
Multiple Constraint ◽  
Typological Variation

AbstractJones argues that extending Seneca kin terms to second cousins requires a revised version of Optimality Theoretic grammar. I extend Seneca terms using three constraints on expression of markers in nested binary classification trees. Multiple constraint rankings on a nested set coupled with local parity checking determines how a given kin classification grammar marks structural endogamy.

Non-binary classification trees

1996 ◽  
Vol 6 (3) ◽  
pp. 231-243 ◽  
Author(s):  
Stanislav Keprta
Keyword(s):  
Binary Classification ◽  
Classification Trees

scholarly journals Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods

2018 ◽  
Vol 66 (5) ◽  
pp. 1347-1356 ◽  
Author(s):  
Michaela Staňková ◽  
David Hampel
Keyword(s):  
Logistic Regression ◽  
Support Vector Machines ◽  
Binary Classification ◽  
Classification Tree ◽  
Classification Trees ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Type I ◽  
Vector Machines ◽  
The Eu

This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.

Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees

2002 ◽  
Vol 30 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Anna K. Jerebko ◽  
Ronald M. Summers ◽  
James D. Malley ◽  
Marek Franaszek ◽  
C. Daniel Johnson
Keyword(s):  
Neural Networks ◽  
Ct Colonography ◽  
Binary Classification ◽  
Classification Trees ◽  
Colonic Polyps ◽  
Computer Assisted ◽  
Computer Assisted Detection

Bases for Strategy Choice and Strategy Transitions in Binary Classification Tasks

2004 ◽  
Author(s):  
Lyle E. Bourne ◽  
Alice F. Healy ◽  
James A. Kole ◽  
William D. Raymond
Keyword(s):  
Binary Classification ◽  
Strategy Choice ◽  
Classification Tasks

Effect of Social Norm on Consumer Demand: Multiple Constraint Approach

2020 ◽  
Vol 22 (1) ◽  
pp. 41-60
Author(s):  
Sungjee Choi ◽  
Inwoo Nam ◽  
Jaehwan Kim
Keyword(s):  
Social Norm ◽  
Consumer Demand ◽  
Multiple Constraint ◽  
Constraint Approach

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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