The virtual lab on Pattern Recognition is design to supplement a senior undergraduate or graduate level course in Statistical Pattern Recognition. The experiments are designed to improve the clarity in understanding of the basic concepts as well as to enable the student to put them into practical use. It is important that the student goes through the objectives and the underlying theory before carrying out the experiments to get maximum benefit.
The primary reference (textbook) for the topics covered by the experiments is: Pattern Classification (2nd ed.), by R.O. Duda, P.E. Hart and D.G. Stork.
The experiments are designed to expose you to certain concepts. You are encouraged to carry out variants of the suggested experiments and learn from them. Please make sure that you read the background material and experiment procedure before carrying out the experiments. Trying out the experiments before this does not hurt, but having the background will help you understand the results better.
Please note:
The simulations of this lab have some of the following software dependencies -
Browsers - Firefox 50 and lower versions, Internet Explorer
Software - Adobe Flash Plugin, Java1.7, Java3D-1.5.2, and IcedTea Plugin.
We recommend you to use our customized Virtual Box for the smooth running of these simulations.
Install VirtualBox - Virtual Labs offers a free download of a customized VirtualBox with the required pre-installed software dependencies (Java 1.7, Java3D-1.5.2, IcedTea Plugin, and Adobe Flash Plugin). Please follow the link to download, install VirtualBox for easy use of Virtual Labs.
Pattern recognition is generally categorized according to the type of learning procedure used to generate the output value.
UG
B. Tech./ B.E in Computer Science & Information Technology
PG
Postgraduate students and Research scholars
The present lab is aligned with courses such as Introduction to Data Structures, Data Structures in C/C++, and the like. The experiments touch on most topics covered in such courses in most curricula.
Universities with Computer Science & Information Technology
Associate Professor,
Software Engineering and Research Centre