The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

文本自动分类是信息检索与数据挖掘领域的研究热点与核心技术, 近年来得到了广泛的关注和
快速的发展. 提出了基于机器学习的文本分类技术所面临的互联网内容信息处理等复杂应用的 …

Clinical datasets are commonly limited in size, thus restraining applications of Machine
Learning (ML) techniques for predictive modelling in clinical research and organ …

Bar charts are an effective way to convey numeric information, but today's algorithms cannot
parse them. Existing methods fail when faced with even minor variations in appearance …

In this paper, we propose a novel hybrid text classification model based on deep belief
network and softmax regression. To solve the sparse high-dimensional matrix computation …

Learning curves are a concept from social sciences that has been adopted in the context of
machine learning to assess the performance of a learning algorithm with respect to a certain …

Motivation Single-centre studies in medical domain are often characterised by limited
samples due to the complexity and high costs of patient data collection. Machine learning …

The metaphor of technical debt was introduced to express the trade off between productivity
and quality, ie, when developers take shortcuts or perform quick hacks. More recently, our …

The class imbalance problem is encountered in real-world applications of machine learning
and results in a classifier's suboptimal performance. Researchers have rigorously studied …