Bayramoglu, Tutku Doruk
Techniques from Machine Learning (ML) have been applied to various mathematical problems in recent years. One such problem is the determination of Hodge numbers of Calabi-Yau varieties. In this thesis, a neural network model to estimate the Hodge numbers of complete intersection Calabi-Yau varieties is built and evaluated.


Deep Learning-Enabled Technologies for Bioimage Analysis
Rabbi, Fazle; Dabbagh, Sajjad Rahmani; Angın, Pelin; Yetisen, Ali Kemal; Tasoglu, Savas (2022-02-01)
Deep learning (DL) is a subfield of machine learning (ML), which has recently demon-strated its potency to significantly improve the quantification and classification workflows in bio-medical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of em...
GALATALI, EGEMEN BERK; ALEMDAR, HANDE; Department of Computer Engineering (2022-8-31)
In this work, we have proposed a new method and ready to use workflow to extract simplified rule sets for a given Machine Learning (ML) model trained on a classifi- cation task. Those rules are both human readable and in the form of software code pieces thanks to the syntax of Python programming language. We have inspired from the power of Shapley Values as our source of truth to select most prominent features for our rule sets. The aim of this work to select the key interval points in given data in order t...
Multiobjective evolutionary feature subset selection algorithm for binary classification
Deniz Kızılöz, Firdevsi Ayça; Coşar, Ahmet; Dökeroğlu, Tansel; Department of Computer Engineering (2016)
This thesis investigates the performance of multiobjective feature subset selection (FSS) algorithms combined with the state-of-the-art machine learning techniques for binary classification problem. Recent studies try to improve the accuracy of classification by including all of the features in the dataset, neglecting to determine the best performing subset of features. However, for some problems, the number of features may reach thousands, which will cause too much computation power to be consumed during t...
Wasserstein generative adversarial active learning for anomaly detection with gradient penalty
Duran, Hasan Ali; Ertekin Bolelli, Şeyda; Department of Computer Engineering (2021-9)
Anomaly detection has become a very important topic with the advancing machine learning techniques and is used in many different application areas. In this study, we approach differently than the anomaly detection methods performed on standard generative models and describe anomaly detection as a binary classification problem. However, in order to train a highly accurate classifier model, the number of anomaly data in data-sets is very limited, and with synthetic data produced using generative models, it ca...
Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2009-06-03)
In Machine Learning (ML) algorithms, one of the crucial issues is the representation of the data. As the data become heterogeneous and large-scale, single kernel methods become insufficient to classify nonlinear data. The finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, we propose a novel method of "infinite" kernel combinations for learning problems with the help of infinite and semi-infinite programming regarding all elements in kernel space. Looking...
Citation Formats
T. D. Bayramoglu, “CALABI-YAU VARIETIES AND MACHINE LEARNING,” M.S. - Master of Science, Middle East Technical University, 2022.