Supervised Machine Learning Techniques: An Overview with Applications to Banking

Prediction of Customer Churn in Banking Industry

With the growing competition in banking industry, banks are required to follow customer retention strategies while they are trying to increase their market share by acquiring new customers. This study compares the performance of six supervised classification techniques to suggest an efficient model to predict customer churn in banking industry, given 10 demographic and personal attributes from 10000 customers of European banks. The effect of feature selection, class imbalance, and outliers will be discussed for ANN and random forest as the two competing models. As shown, unlike random forest, …

An Overview of Deep Semi-Supervised Learning

Deep neural networks demonstrated their ability to provide remarkable performances on a wide range of supervised learning tasks (e.g., image classification) when trained on extensive collections of labeled data (e.g., ImageNet). However, creating such large datasets requires a considerable amount of resources, time, and effort. Such resources may not be available in many practical cases, limiting the adoption and the application of many deep learning methods. In a search for more data-efficient deep learning methods to overcome the need for large annotated datasets, there is a rising research …

Partial differential equation regularization for supervised machine learning

This article is an overview of supervised machine learning problems for regression and classification. Topics include: kernel methods, training by stochastic gradient descent, deep learning architecture, losses for classification, statistical learning theory, and dimension independent generalization bounds. Implicit regularization in deep learning examples are presented, including data augmentation, adversarial training, and additive noise. These methods are reframed as explicit gradient regularization.

半监督学习、随机拔河博弈与 p-Laplacian 的一致性

Consistency of semi-supervised learning, stochastic tug-of-war games, and the p-Laplacian

本文概述了偏微分方程与基于图的半监督学习的交叉领域，聚焦PDE连续统极限证明算法适定性，并利用p-Laplacian的随机拔河博弈解释提出了其半监督学习一致性的新结果，辅以数值实验说明。

Machine learning in resting-state fMRI analysis

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature represent…

深度演化半监督异常检测

Deep evolving semi-supervised anomaly detection

论文形式化持续半监督异常检测任务，提出VAE基线与带离群点拒绝的深度生成回放方法，并验证EVT和EWC对比下的有效性。