To date, many active learning techniques have been developed for acquiring labels when training data is limited. However, an important aspect of the problem has often been neglect...
Learning from noisy data is a challenging and reality issue for real-world data mining applications. Common practices include data cleansing, error detection and classifier ensemb...
Yan Zhang, Xingquan Zhu, Xindong Wu, Jeffrey P. Bo...
Class membership probability estimates are important for many applications of data mining in which classification outputs are combined with other sources of information for decisi...
Recent work has shown promise in using large, publicly available, hand-contributed commonsense databases as joint models that can be used to infer human state from day-to-day sens...
William Pentney, Matthai Philipose, Jeff A. Bilmes...
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task ...