Machine Learning is a scientific discipline that deals with the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions.
Machine learning can be considered a subfield of computer science and statistics. It has strong ties to artificial intelligence and optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition "can be viewed as two facets of the same field.
Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are:
We are currently working on the semi-supervised framework and its application in general to recognition and segmentation of images. In general, automatic recognition system requires extraction of robust features from the face images in the first step. Then the classification of these images is done by using machine learning tools on the extracted features. In the process we are trying to propose extension of Twin Support Vector Machines for segmentation, recognition, regression, clustering and classification.
Area Contact: Dr Reshma Khemchandani
International Workshop on Soft Computing and its Applications to be held on during 25-27 March 2015.
International Workshop on Data And Text Analytics to be held from 8th to 13th December 2014.
CSIR Sponsored Symposium on Image Processing and pattern Recognition (S-IPPR-2013) 31st Oct-1st Nov,2013. (Poster)
2013 Workshop: International Workshop on Machine Learning and Text Analytics, 15-23 Dec. 2013