Semi-supervised learning algorithms commonly incorporate the available background knowledge such that an expression of the derived model’s quality is improved. Depending on the specific context quality can take several forms and can be related to the generalization performance or to a simple clustering coherence measure. Recently, a novel perspective of semi-supervised learning has been put for...

Purpose: Completely labeled datasets of pathology slides are often difficult and time consuming to obtain. Semi-supervised learning methods are able to learn reliable models from small number of labeled instances and large quantities of unlabeled data. In this paper, we explored the potential of clustering analysis for semi-supervised support vector machine (SVM) classifier. Method: A clusterin...

In this paper we investigate an application of feature clustering for word sense disambiguation, and propose a semisupervised feature clustering algorithm. Compared with other feature clustering methods (ex. supervised feature clustering), it can infer the distribution of class labels over (unseen) features unavailable in training data (labeled data) by the use of the distribution of class labe...

We study semi-supervised learning when the data consists of multiple intersecting manifolds. We give a finite sample analysis to quantify the potential gain of using unlabeled data in this multi-manifold setting. We then propose a semi-supervised learning algorithm that separates different manifolds into decision sets, and performs supervised learning within each set. Our algorithm involves a n...

Abstract In this paper, we propose a semi-supervised method to cluster unstructured textual data called sentiment clustering on natural language texts. The aim is identify clusters homogeneous with respect the overall of texts analyzed. combines different techniques and methodologies: Sentiment Analysis, Threshold-based Naïve Bayes classifier, Network-based Semi-supervised Clustering. It involv...