Advanced classification techniques for image and text analysis based on clustering
Dr. Parag Kulkarni (firstname.lastname@example.org)
is Ph.D. form IIT, Kharagpur (www.iitkgp.ernet.in).
He is working in IT industry for more than 13 years. He is on research
panel and Ph.D. guide for University of Pune, BITS and Symbiosis deemed
University. He has conducted 5 tutorials at various international conferences
and was a keynote speaker for three international conferences.
In all learning techniques and decision methods we need to classify various objects, behaviors and patterns. In this tutorial we will discuss advanced classification algorithms in detail. We will talk about various pattern and behavior based methods used for text and image classification. The tutorial will also cover usage of various methods like SVM, intelligent feature extraction; feature based clustering for these applications. In this tutorial we will cover various difficulties in classification and learning methodologies,which can handle nonlinear behavior very effectively. The tutorial will also present industrial applications of classification and case studies for classifying images and textual data. It will also throw some light on how these techniques can be extended for other applications in decision engineering.
This tutorial is formulated with following objectives:
When I was working with decision engineering, decision support systems for almost 13 years in various industries, I realized that these all things could be put together and can be shared with various audiences. That gave me inspiration to present tutorial and I started sharing experiences by means of tutorials and learned a lot through this exercise.
Classification is complex process. Classification is simple as long as it is linear. But most of the problems are non-linear and there is lot of noise. While applying the techniques we need to work on various parameters and it also requires tuning and tweaking. Here in this tutorial we are going to present step-by-step approach for classification and which ultimately leads to decision-making or can be part of decision support systems. We will also talk about clustering, outlier removal and other real life scenario without which the discussion may not be completed.
Duration: 4.00 HR
Some background in statistics