Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Probabilistic recognition of orthogonal and symplectic groups
Download
114992.pdf
Date
2001
Author
Yalçınkaya, Şükrü
Metadata
Show full item record
Item Usage Stats
117
views
0
downloads
Cite This
URI
https://hdl.handle.net/11511/10908
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Probabilistic Distance Clustering: Algorithm and Applications
İyigün, Cem (World Scientific , 2009-02-01)
The probabilistic distance clustering method of the authors [2, 8], assumes the cluster membership probabilities given in terms of the distances of the data points from the cluster centers, and the cluster sizes. A resulting extremal principle is then used to update the cluster centers (as convex combinations of the data points), and the cluster sizes (if not given.) Progress is monitored by the joint distance function (JDF), a weighted harmonic mean of the above distances, that approximates the data by cap...
Probabilistic Forecasting of Multiple Time Series with Single Recurrent Neural Network
TOPALLAR, SARP TUĞBERK; Yozgatlıgil, Ceylan; Department of Scientific Computing (2022-9-20)
Time series forecasting can be summarized as predicting the future values of a sequence indexed by timestamps based on the past records of that sequence. Optimal or near-optimal resource allocation requires accurate predictions into the future. The study presents investigation performed on both classical methods and more contemporary methods from the literature. The classical methods studied are Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS) and Seasonal-Trend decomposition us...
Probabilistic distance clustering on networks
İyigün, Cem (null; 2018-11-05)
In this study, a soft clustering problem on networks is investigated. It is assumed that cluster centers are located not only on vertices, but also on the edges of the network. Two different soft assignment schemes are studied where different membership functions are considered for the assignments. Structural properties for the clustering problem have been derived for different problem settings and the relation to the p-median problem has been shown under specific conditions. As a solution approach, a metah...
Probabilistic analysis of bridge networks based on system reliability and Monte Carlo simulation
Akgül, Ferhat (null; 2003-07-09)
Probabilistic performance prediction modeling for bridges considering maintenance effects within a combined computation, visualization and programming environment
Akgül, Ferhat (2010-07-15)
Various models have been developed in recent past for lifetime performance prediction of bridges. Studies for developing new theoretical models and improvement of the existing ones are part of ongoing research in bridge deterioration and management. However, improvement of such theoretical models so that they can be practically implemented in bridge management systems is a challenging task. In order to achieve such task, newly developed models must be tested and further improved to achieve practicality. Lif...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
Ş. Yalçınkaya, “Probabilistic recognition of orthogonal and symplectic groups,” Middle East Technical University, 2001.