T. Koteswara rao

Learn More
This paper presents an approach to classify remote sensed data using a hybrid classifier. Random forest, Support Vector machines and boosting methods are used to build the said hybrid classifier. The central idea is to subdivide the input data set into smaller subsets and classify individual subsets. The individual subset classification is done using(More)
These algorithms have been applied to an extensive number of problems including noise and echo cancelling, channel equalization, signal prediction, adaptive arrays as well as many others. Noise control is the field of acoustical engineering that deals with reducing unwanted sound in the environment An active noise control (ANC) system based on adaptive(More)
Study of remote sensed imagery has gained practical significance in various domains such as environmental monitoring, fire risk mapping, change detections and land use. Classification is a data mining methodology which is used to assign class labels to data instances and build a model so as to be able to predict class labels for unlabelled data. In this(More)
  • 1