Mayer Aladjem 
Associate Professor of Electrical & Computer Engineering

  Short biography  

Research Interests:  
Statistical pattern recognition and machine learning.  
Neural networks for pattern recognition.  
Multivariate data analysis, feature extraction and reduction.

 

                             Address:
                             Ben-Gurion University of the Negev,
                             Department of Electrical and Computer engineering,
                             P.O.Box 653,
                             Beer-Sheva, 84105, Israel

                             E-mail: aladjem@ee.bgu.ac.il
                             Phone: 972-8-6472409

                             Place: Room 114 / Building 33
                             Meetings on request by e-mail.

Graduate Pattern Recognition (Machine Learning) courses (2019-2020)

Semester "Bet" 2019 : Pattern Recognition, 36125321.

Semester "Aleph" 2020 : Neural Networks for Pattern Recognition - Statistical foundation, perspective and alternatives, 36125651.

 The courses are suitable for all fields of engineering specializations.  
Pattern recognition is a practical technology, with successful applications in many fields.  
The courses are intended to be largely self-contained.


                        Additional Information: Editor   Chairman   Lectures   Committees

                          Research Lab: Pattern Recognition and Intelligent Data Analysis (PRIDA)

                        PhD and M.Sc. students (List of the theses and journal publications)

                        Publications (1991-Present) in: Journals   Conference proceedings

                        Courses: Graduate level   Undergraduate level


                           Synopsis of Research   (This part is in reconstruction and extension)

· Novel discriminant criteria
· Interactive system for exploratory data analysis
· Method for estimating the significance of the control parameters of projection procedures
· Multiclass discriminant projections
· New methods for successive optimization of the discriminant criteria
· Comparative study of neural networks for multivariate data projection
· Discriminant analysis via neural network reduction of the class separation