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Belief propagation is an iterative algorithm for computing marginals of functions on a graphical model most commonly used in information retrieval. In this paper, we consider the problem of performing cross-domain belief propagation on multi-relational data for semi-supervised learning. We demonstrate that partial knowledge on one type of variables can help knowledge discovery on the other type of...
Multi-task learning refers to the learning problem of performing inference by jointly considering multiple related tasks. There have already been many research efforts on supervised multi-task learning. However, collecting sufficient labeled data for each task is usually time consuming and expensive. In this paper, we consider the semi-supervised multitask learning (SSMTL) problem, where we are given...
Linear discriminant analysis (LDA) is a popular feature extraction method that has aroused considerable interests in computer vision and pattern recognition fields. The projection vectors of LDA is usually achieved by maximizing the between-class scatter and simultaneously minimizing the within-class scatter of the data set. However, in practice, there is usually a lack of sufficient labeled data,...
In teaching practice, it is hard to accurately, systematically evaluating how well students master the knowledge.The reason that students can not understand the current concept is that prerequisite knowledge hasn't been grasped, and students also do not know the potential impact on the following course. It is difficult to implement trace and personalized analysis. The intelligence tutoring system...
Traditional clustering approaches usually analyze static datasets in which objects are kept unchanged after being processed, but many practical datasets are dynamically modified which means some previously learned patterns have to be updated accordingly. Re-clustering the whole dataset from scratch is not a good choice due to the frequent data modifications and the limited out-of-service time, so...
Nonnegative matrix factorization (NMF) is a versatile model for data clustering. In this paper, we propose several NMF inspired algorithms to solve different data mining problems. They include (1) multi-way normalized cut spectral clustering, (2) graph matching of both undirected and directed graphs, and (3) maximal clique finding on both graphs and bipartite graphs. Key features of these algorithms...
Customizable and extensible processors can efficiently meet the growing demand of application-specific IC device designs in performance and flexibility. Due to the increasing complexity of software applications, it is essential to automatically decide operations to be carried out in custom function units from high-level application code. This paper addresses efficient techniques for identifying application-specific...
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