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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Enhancement to Selective Incremental Approach for Transductive Nearest Neighbour Classification

Enhancement to Selective Incremental Approach for Transductive Nearest Neighbour Classification

Reddy, E. Madhusudhana

Paperback

Crafts GeneralGeneral Computers

ISBN10: 3656342504
ISBN13: 9783656342502
Publisher: Grin Verlag
Published: Dec 31 2012
Pages: 140
Weight: 0.42
Height: 0.33 Width: 5.83 Depth: 8.27
Language: English
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, ID3, Nearest 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.

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