Machine learning in materials science
Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …
method and the density functional theory (DFT)‐based method, are unable to keep pace …
Cell death discrimination with Raman spectroscopy and support vector machines
G Pyrgiotakis, OE Kundakcioglu, K Finton… - Annals of biomedical …, 2009 - Springer
In the present study, Raman spectroscopy is employed to assess the potential toxicity of
chemical substances. Having several advantages compared to other traditional methods …
chemical substances. Having several advantages compared to other traditional methods …
[图书][B] Data mining and mathematical programming
PM Pardalos, P Hansen - 2008 - books.google.com
Data mining aims at finding interesting, useful or profitable information in very large
databases. The enormous increase in the size of available scientific and commercial …
databases. The enormous increase in the size of available scientific and commercial …
Raman spectroscopy and support vector machines for quick toxicological evaluation of titania nanoparticles
G Pyrgiotakis, OE Kundakcioglu… - Journal of Raman …, 2011 - Wiley Online Library
With the rapid development of nanotechnology products, there is a significant concern on
the adverse effects that might be associated with them. Traditional biological assays are …
the adverse effects that might be associated with them. Traditional biological assays are …
Incremental classification with generalized eigenvalues
Supervised learning techniques are widely accepted methods to analyze data for scientific
and real world problems. Most of these problems require fast and continuous acquisition of …
and real world problems. Most of these problems require fast and continuous acquisition of …
[PDF][PDF] Classification via mathematical programming
PM Pardalos, OE Kundakcioglu - Appl. Comput. Math, 2009 - researchgate.net
This survey concerns applications of mathematical programming in the context of
classification. We mainly discuss two supervised learning methods: Support Vector …
classification. We mainly discuss two supervised learning methods: Support Vector …
[PDF][PDF] Selective linear and nonlinear classification
O Seref, OE Kundakcioglu… - CRM Proceedings and …, 2008 - researchgate.net
We introduce a generalized support vector classification problem: Let Xi, i= 1,..., n be
mutually exclusive sets of pattern vectors such that all pattern vectors xi, k, k= 1,...,| Xi| have …
mutually exclusive sets of pattern vectors such that all pattern vectors xi, k, k= 1,...,| Xi| have …
Secondary structure classification of isoform protein markers in oncology
G Patrizi, C Cifarelli, V Losacco, G Patrizi - Mathematical Approaches to …, 2011 - Springer
The determination of the secondary structure of proteins can be considered a relevant
procedure to characterise isoform protein markers for cancer and other pathologies. Their …
procedure to characterise isoform protein markers for cancer and other pathologies. Their …
Nonlinear Recognition Methods for Oncological Pathologies
G Patrizi, V Pietropaolo, A Carbone… - Data Mining for …, 2012 - Springer
A biomarker, or biological marker is a substance used as an indicator of a biological state. It
is used in many scientific fields. The determination and function of the biomarker can be …
is used in many scientific fields. The determination and function of the biomarker can be …
[引用][C] Combinatorial and Nonlinear Optimization Techniques in Pattern Recognition with Applications in Healthcare
OE Kundakcioglu - 2009 - University of Florida