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Learning a metric when clustering data points in the presence of constraints
, Article Advances in Data Analysis and Classification ; Volume 14, Issue 1 , 2020 , Pages 29-56 ; Bashiri, M. A ; Beigy, H ; Sharif University of Technology
Springer
2020
Abstract
Learning an appropriate distance measure under supervision of side information has become a topic of significant interest within machine learning community. In this paper, we address the problem of metric learning for constrained clustering by considering three important issues: (1) considering importance degree for constraints, (2) preserving the topological structure of data, and (3) preserving some natural distribution properties in the data. This work provides a unified way to handle different issues in constrained clustering by learning an appropriate distance measure. It has modeled the first issue by injecting the importance degree of constraints directly into an objective function....
Genetic algorithms for fuzzy multi-objective approach to portfolio selection
, Article Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, 12 July 2010 through 14 July 2010 ; July , 2010 ; 9781424478576 (ISBN) ; Nikkholgh, R ; Gharahkozli, H ; Sharif University of Technology
2010
Abstract
This research deals with a model with better efficiency for selection of portfolio making use of cardinal constraints, which are explained in previous sections. Such a method, which is a combination of fuzzy models and MCDM considering the constraints intended by investors, has not been used in previous models. We have considered transactions cost, because they are among factors important for an investor, and their being ignored in a portfolio selection method will result in inefficient portfolio. Sector value constraint is among other constraints considered here. Such a constraint aims to raise investment rate in sectors with higher values. Cardinal constraints (number of shares existing in...
Multidisciplinary optimization of a car component under NVH and weight constraints using RSM
, Article 2009 ASME International Mechanical Engineering Congress and Exposition, IMECE2009, Lake Buena Vista, FL, 13 November 2009 through 19 November 2009 ; Volume 15 , 2010 , Pages 315-319 ; 9780791843888 (ISBN) ; Zahedi, F ; Azadi, S ; Moradi, M ; Sharif University of Technology
American Society of Mechanical Engineers (ASME)
2010
Abstract
One of the important challenges in the auto industry is to reduce the mass of the vehicle while meeting structural performance requirements for Crashworthiness, Noise, Vibration and Harshness (NVH) etc. In this paper, a multidisciplinary optimization (MDO) of a car back-bonnet is investigated by using trie Response Surface Method (RSM). Firstly, a car body is fully surface modeled in CATIA and meshed in HYPERMESH software. Then, modal analysis of the finite element model is performed by NASTRAN software to find natural frequencies. Frequency map of that component is extracted and compared with a reference map to detect defects. Design of Experiments (DOE) methodologies is used for a...