Managing, processing and understanding big healthcare data is challenging, costly and
demanding. Without a robust fundamental theory for representation, analysis and inference …

Completely labeled pathology datasets are often challenging and time-consuming to obtain.
Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points …

Background Microbial inhabitants of soils are important to ecosystem and planetary
functions, yet there are large gaps in our knowledge of their diversity and ecology. The …

Having a multitude of unlabeled data and few labeled ones is a common problem in many
practical applications. A successful methodology to tackle this problem is self-training semi …

Statistical techniques are important tools in modeling research work. However, there could
be misleading outcomes if sufficient care is undermined in choosing the right approach …

The goal of sentiment analysis is to determine opinions, emotions, and attitudes presented
in source material. In tweet sentiment analysis, opinions in messages can be typically …

Self-labeled technique, a paradigm of semisupervised classification (SSC), is highly
effective in alleviating the shortage of labeled data in classification tasks via an iterative self …

There is a certain belief among data science researchers and enthusiasts alike that
clustering can be used to improve classification quality. Insofar as this belief is fairly …

This paper demonstrates a new water end-use disaggregation and classification tool that
builds on existing end-use disaggregation studies and addresses the unavailability of code …

Self-training is a commonly semi-supervised learning Algorithm framework. How to select
the high-confidence samples is a crucial step for algorithms based on self-training …