IEEE

Learning Contrastive Evolving Micro-Clusters for Robust Semi-Supervised Data Stream Classification

Abstract: Semi-supervised learning on data streams with concept drift has attracted considerable attention in recent decades. Many existing algorithms have achieved promising results on ...
IEEE

Heterogeneous Covariates-Aware Pseudo Supervised Meta-Learning for Few-shot Diabetes Classification

Abstract: Objective: The limited labeled data hinders the application of medical artificial intelligence technology in the field of diabetes classification. In this paper, a pseudo-label supervised ...
Scientific Research Publishing

Comparative Analysis of Machine Learning Algorithms for Optimal Land Use and Land Cover Classification: Guiding Method Selection for Resource-Limited Settings in Tiaty, Baringo ...

Arid and semiarid regions face challenges such as bushland encroachment and agricultural expansion, especially in Tiaty, Baringo, Kenya. These issues create mixed opportunities for pastoral and ...
Frontiers

Contrastive self-supervised learning for neurodegenerative disorder classification

1 German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany 2 Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany Introduction: Neurodegenerative ...
Scientific Research Publishing

Kipf, T.N. and Welling, M. (2017) Semi-Supervised Classification with Graph Convolutional Networks.

ABSTRACT: Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph ...
Frontiers

SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification

Introduction: In clinical, the echocardiogram is the most widely used for diagnosing heart diseases. Different heart diseases are diagnosed based on different views of the echocardiogram images, so ...