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Now showing 1 - 6 of 6

Citation - Scopus: 4
Robust Principal Component Analysis by Reverse Iterative Linear Programming
(Springer Verlag, 2016) Visentin, A.; Prestwich, S.; Armagan Tarim, S.
Principal Components Analysis (PCA) is a data analysis technique widely used in dimensionality reduction. It extracts a small number of orthonormal vectors that explain most of the variation in a dataset, which are called the Principal Components. Conventional PCA is sensitive to outliers because it is based on the L2-norm, so to improve robustness several algorithms based on the L1-norm have been introduced in the literature. We present a new algorithm for robust L1- norm PCA that computes components iteratively in reverse, using a new heuristic based on Linear Programming. This solution is focused on finding the projection that minimizes the variance of the projected points. It has only one parameter to tune, making it simple to use. On common benchmarks it performs competitively compared to other methods. The data and software related to this paper are available at https://github. com/visentin-insight/L1-PCAhp. © Springer International Publishing AG 2016.
Citation - Scopus: 10
Effectiveness of Smes in Turkish Economy and Agricultural Industry Sector
(2004) Baykal, N.A.; Gunes, E.
The latest developments and modulations in Turkish economy, adaptation process to European Union and entering into international markets, risks and uncertainties in these markets and some opportunities may affect SMEs in positive and negative aspects. In fact, new areas may open and some business sectors may slowly disappear. As part of the adaptation process with EU, SMEs deserve important order on the agenda while trying to enter international markets but are left to their fate in Turkey. In this article, the existing situations, problems and contributions of SMEs in industrial and agricultural sector to Turkish economy will be analysed, then policies that must be put in practice for more contribution to Turkish economy will be dwelt upon. Finally, solutions to these situations will be proposed. © 2004 Inderscience Enterprises Ltd.
Citation - WoS: 1
Citation - Scopus: 1
Randomness as a Constraint
(Springer-verlag Berlin, 2015) Prestwich, Steven D.; Rossi, Roberto; Tarim, S. Armagan
Some optimisation problems require a random-looking solution with no apparent patterns, for reasons of fairness, anonymity, undetectability or unpredictability. Randomised search is not a good general approach because problem constraints and objective functions may lead to solutions that are far from random. We propose a constraint-based approach to finding pseudo-random solutions, inspired by the Kolmogorov complexity definition of randomness and by data compression methods. Our "entropy constraints" can be implemented in constraint programming systems using well-known global constraints. We apply them to a problem from experimental psychology and to a factory inspection problem.
A New Clustering Method With Fuzzy Approach Based on Takagi-Sugeno Model in Queuing Systems
(IGI Global, 2013) Zanjanbar, F.G.; Şentarli, I.
In this paper, the authors propose a new hard clustering method to provide objective knowledge on field of fuzzy queuing system. In this method, locally linear controllers are extracted and translated into the first-order Takagi-Sugeno rule base fuzzy model. In this extraction process, the region of fuzzy subspaces of available inputs corresponding to different implications is used to obtain the clusters of outputs of the queuing system. Then, the multiple regression functions associated with these separate clusters are used to interpret the performance of queuing systems. An application of the method also is presented and the performance of the queuing system is discussed. Copyright © 2013, IGI Global.
Citation - WoS: 4
Citation - Scopus: 4
Fixed Point Results Via Simulation Functions in the Context of Quasi-Metric Space
(Univ Nis, Fac Sci Math, 2018) Fulga, Andreea; Tas, Aysegul
In this paper, we investigate the existing non-unique fixed points of certain mappings, via simulation functions in the context of quasi-metric space. Our main results generalize and unify several existing results on the topic in the literature.
Citation - WoS: 1
Citation - Scopus: 2
A New Causal Discovery Heuristic
(Springer, 2018) Tarim, S. A.; Ozkan, I.; Prestwich, S. D.
Probabilistic methods for causal discovery are based on the detection of patterns of correlation between variables. They are based on statistical theory and have revolutionised the study of causality. However, when correlation itself is unreliable, so are probabilistic methods: unusual data can lead to spurious causal links, while nonmonotonic functional relationships between variables can prevent the detection of causal links. We describe a new heuristic method for inferring causality between two continuous variables, based on randomness and unimodality tests and making few assumptions about the data. We evaluate the method against probabilistic and additive noise algorithms on real and artificial datasets, and show that it performs competitively.

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