Enhancement to Selective Incremental Approach for Transductive Nearest Neighbour Classification

Master's Thesis from the year 2012 in the subject Computer Science - Didactics course: COMPUTER SCIENCE & ENGINEERING language: English abstract: During the last years semi-supervised learning has emerged as an exciting new direction in machine learning research. It is closely related to profound issues of how to do inference from data as witnessed by its overlap with transductive inference. Semi-Supervised learning is the half-way between Supervised and Unsupervised Learning. In this majority of the patterns are unlabelled they are present in Test set and knowed labeled patterns are present in Training set. Using these training set we assign the labels for test set. Here our Proposed method is using Nearest Neighbour Classifier for Semi-Supervised learning we can label the unlabelled patterns using the labeled patterns and then compare these method with the traditionally Existing methods as graph mincut spectral graph partisan ID3Nearest Neighbour Classifier and we are going to prove our Proposed method is more scalable than the Existing methods and reduce time complexity of SITNNC(Selective Incremental Approach for Transductive Nearest Neighbour Classifier) using Leaders Algorithm.