Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …

The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …

Variable selection plays an important role in high dimensional statistical modelling which
nowadays appears in many areas and is key to various scientific discoveries. For problems …

The year 2015 marks the 30th birthday of DC (Difference of Convex functions) programming
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …

Support Vector Machines (SVMs) have been one of the most successful machine learning
techniques for the past decade. For anomaly detection, also a semi-supervised variant, the …

Data augmentation is a widely used trick when training deep neural networks: in addition to
the original data, properly transformed data are also added to the training set. However, to …

We discuss binary classification from only from positive and unlabeled data (PU
classification), which is conceivable in various real-world machine learning problems. Since …

In the fraud graph, fraudsters often interact with a large number of benign entities to hide
themselves. So, there are not only the homophilic connections formed by the same label …

Support vector machines (SVM), as one of effective and popular classification tools, have
been widely applied in various fields. However, they may incur prohibitive computational …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …