'This report addresses a partially supervised classification problem, especially when the class definition and corresponding training samples are provided a pnori only for just one particular class. In practical applications of pattern classification techniques, a frequently observed characteristic is the heavy, often nearly impossible requirements on representative prior statistical class char...

In this paper, we propose a novel method to enrich the representation provided to the output layer of feedforward neural networks in the form of an auto-clustering output layer (ACOL) which enables the network to naturally create sub-clusters under the provided main class labels. In addition, a novel regularization term is introduced which allows ACOL to encourage the neural network to reveal i...

In our data driven world, clustering is of major importance to help end-users and decision makers understanding information structures. Supervised learning techniques rely on ground truth to perform the classification and are usually subject to overtraining issues. On the other hand, unsupervised clustering techniques study the structure of the data without disposing of any training data. Given...

Background: The prevalence of drug resistance was higher in areas with poor tuberculosis control. Resistance to Anti tuberculosis treatment was found in all 35 countries and regions surveyed. Objective: To Compare of Drug Resistance pattern in cases under supervised vs unsupervised treatment. Study design: Hospital based study. Setting: Department of Pulmonary Medicine, Motilal Nehru Medical Co...

In this paper, we apply the combination method of bagging which has been developed in the context of supervised learning of classifiers and regressors to the unsupervised artificial neural network known as the Self Organising Map. We show that various initialisation techniques can be used to create maps which are comparable by humans by eye. We then use a semi-supervised version of the SOM to c...

This Paper represents a literature review of Swarm intelligence algorithm in the area of semi-supervised classification. There are many research papers for applying swarm intelligence algorithms in the area of machine learning. Some algorithms of SI are applied in the area of ML either solely or hybrid with other ML algorithms. SI algorithms are also used for tuning parameters of ML algorithm, ...

EVERITT, J.H.; YANG, C.; SRIHARAN, S., and JUDD, F.W., 2009. Using high resolution satellite imagery to map black mangrove on the Texas Gulf Coast. Journal of Coastal Research, 24(6), 1582–1586. West Palm Beach (Florida), ISSN 0749-0208. QuickBird false color satellite imagery was evaluated for distinguishing black mangrove [Avicennia germinans (L.) L.] populations on the south Texas Gulf Coast...

The detection of ischemic episodes is a difficult pattern classification problem. The motivation for developing the Supervising Network Self Organizing Map (sNet-SOM) model is to design computationally effective solutions for the particular problem of ischemia detection and other similar applications. The sNet-SOM uses unsupervised learning for the regions where the classification is not ambigu...

A verb paradigm is a set of inflectional categories for a single verb lemma. To obtain verb paradigms we extracted left and right bigrams for the 400 most frequent verbs from over 100 million words of text, calculated the Kullback Leibler distance for each pair of verbs for left and right contexts separately, and ran a hierarchical clustering algorithm for each context. Our new method for findi...