The role of cognitive functions in the diagnosis of bipolar disorder: a machine learning model
Background Considering the clinical heterogeneity of the bipolar disorder, difficulties are
encountered in making the correct diagnosis. Although a number of common findings have …
encountered in making the correct diagnosis. Although a number of common findings have …
Video based face recognition by using discriminatively learned convex models
A majority of the image set based face recognition methods use a generatively learned
model for each person that is learned independently by ignoring the other persons in the …
model for each person that is learned independently by ignoring the other persons in the …
Polyhedral conic kernel-like functions for SVMs
In this study, we propose a new approach that can be used as a kernel-like function for
support vector machines (SVMs) in order to get nonlinear classification surfaces. We …
support vector machines (SVMs) in order to get nonlinear classification surfaces. We …
A random subspace based conic functions ensemble classifier
E Cimen - Turkish Journal of Electrical Engineering and …, 2020 - journals.tubitak.gov.tr
Classifiers overfit when the data dimensionality ratio to the number of samples is high in a
dataset. This problem makes a classification model unreliable. When the overfitting problem …
dataset. This problem makes a classification model unreliable. When the overfitting problem …
[HTML][HTML] ICF: An algorithm for large scale classification with conic functions
Abstract Incremental Conic Functions (ICF) algorithm is developed for solving classification
problems based on mathematical programming. This algorithm improves previous version of …
problems based on mathematical programming. This algorithm improves previous version of …
Revised polyhedral conic functions algorithm for supervised classification
G Ceylan, G Öztürk - Turkish Journal of Electrical Engineering …, 2020 - journals.tubitak.gov.tr
In supervised classification, obtaining nonlinear separating functions from an algorithm is
crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) …
crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) …
[PDF][PDF] A comparative review of incremental clustering methods for large dataset
APS Kushwaha, S Jaloreeb, RS Thakurc - International Journal, 2021 - academia.edu
Several algorithms have developed for analyzing large incremental datasets. Incremental
algorithms are relatively efficient in dynamic evolving environment to seek out small clusters …
algorithms are relatively efficient in dynamic evolving environment to seek out small clusters …
The nearest polyhedral convex conic regions for high-dimensional classification
In the nearest-convex-model type classifiers, each class in the training set is approximated
with a convexclass model, and a test sample is assigned to a class based on the shortest …
with a convexclass model, and a test sample is assigned to a class based on the shortest …
[PDF][PDF] Multi center polyhedral conic classifiers that can classify complex data
H Sağlamlar - Journal of the Faculty of Engineering and …, 2021 - scholar.archive.org
Multi center polyhedral conic classifiers that can classify complex data Page 1 Journal of the
Faculty of Engineering and Architecture of Gazi University 36:4 (2021) 1817-1830 Multi center …
Faculty of Engineering and Architecture of Gazi University 36:4 (2021) 1817-1830 Multi center …
Low-dimensional Interpretable Kernels with Conic Discriminant Functions for Classification
G Ceylan, SI Birbil - ar** functions that show impressive
predictive power due to their high-dimensional feature space representations. In this study …
predictive power due to their high-dimensional feature space representations. In this study …