Supervised learning on sequence data, also known as sequence classification, has been well recognized as an important data mining task with many significant applications. Since te...
Zhengzheng Xing, Jian Pei, Guozhu Dong, Philip S. ...
Low-rank approximations of the adjacency matrix of a graph are essential in finding patterns (such as communities) and detecting anomalies. Additionally, it is desirable to track ...
In some applications such as filling in a customer information form on the web, some missing values may not be explicitly represented as such, but instead appear as potentially va...
In traditional classification setting, training data are represented as a single table, where each row corresponds to an example and each column to a predictor variable or the targ...
Abstract. Gaussian processes have successfully been used to learn preferences among entities as they provide nonparametric Bayesian approaches for model selection and probabilistic...