EachWiki: Suggest to Be an Easy-To-Edit Wiki Interface for Everyone (2009)
Huajie Zhang, Linyun Fu, Haofen Wang, Haiping Zhu, Yang Wang, Yong Yu
Abstract. In this paper, we present EachWiki, an extension of Semantic MediaWiki characterized by an intelligent suggestion mechanism. It aims to facilitate the wiki authoring by recommending the...
Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring (2008)
Linyun Fu, Haofen Wang, Haiping Zhu, Huajie Zhang, Yang Wang, Yong Yu
Abstract. Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It can also serve as an ideal Semantic Web data source due to...
Charles X. Ling, Huajie Zhang, Carla Brodley, Andrea Danyluk
One of the most important fundamental properties of Bayesian networks is the representational power, re ecting what kind of functions they can or cannot represent. In this paper, we establish an...
Representational Upper Bounds of Bayesian Networks (2007)
Huajie Zhang Hzhang, Huajie Zhang, Charles X. Ling
One of the fundamental issues of Bayesian networks is their representational power, reflecting what kind of functions they can or cannot represent. In this paper, we first prove an upper bound on the...
Huajie Zhang, Charles X. Ling, Zhiduo Zhao
Abstract. Naive Bayes is an efficient and effective learning algorithm, but previous results show that its representation ability is severely limited since it can only represent certain linearly...
Charles Ling Ling, Charles X. Ling, Huajie Zhang, E. Brodley, Andrea Danyluk
One of the most important fundamental properties of Bayesian networks is the representational power, reflecting what kind of functions they can or cannot represent. In this paper, we establish an...
The Representational Power of Discrete Bayesian Networks (2002)
Charles X. Ling, Huajie Zhang, E. Brodley, Andrea Danyluk
One of the most important fundamental properties of Bayesian networks is the representational power, reflecting what kind of functions they can or cannot represent. In this paper, we establish an...
Geometric properties of Naive Bayes in nominal domains (2001)
Numerous approaches to learning classifiers, such as decision trees, neural networks, and instance-based learning, have been studied. In recent years, probability approaches to learning classifiers...
An improved learning algorithm for augmented naive Bayes (2001)
Abstract. Data mining applications require learning algorithms to have high predictive accuracy, scale up to large datasets, and produce comprehensible outcomes. Naive Bayes classifier has received...
Learnability of Augmented Naive Bayes in Nominal Domains (2001)
It is well-known that Naive Bayes can only represent linearly separable functions in binary domains. But the learnability of general Augmented Naive Bayes is open. Little work is done on the...
Mining generalized query patterns from web logs (2001)
Charles X. Ling, Jianfeng Gao, Huajie Zhang
User logs of a popular search engine keep track of user activities including user queries, user click-through from the returned list, and user browsing behaviors. Knowledge about user queries...
Mining generalized query patterns from web logs (2001)
Charles X. Ling, Jianfeng Gao, Huajie Zhang
User logs of a popular search engine keep track of user activities including user queries, user click-through from the returned list, and user browsing behaviors. Knowledge about user queries...