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Proceedings Of The 18th Irish Conference On Artificial Intelligence And Cognitive Science, Sarah Jane Delany, Michael Madden
Proceedings Of The 18th Irish Conference On Artificial Intelligence And Cognitive Science, Sarah Jane Delany, Michael Madden
Books/Book Chapters
These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI).
Semi-Supervised Classification With Hybrid Generative/Discriminative Methods, Gregory Druck, Chris Pal, Xiaoping Zhu, Andrew Mccallum
Semi-Supervised Classification With Hybrid Generative/Discriminative Methods, Gregory Druck, Chris Pal, Xiaoping Zhu, Andrew Mccallum
Andrew McCallum
In this paper, we study semi-supervised learning using hybrid generative/discriminative methods. Specifically, we compare two recently proposed frameworks for combining generative and discriminative classifiers and apply them to semi-supervised classification. In both cases we explore the tradeoff between maximizing a discriminative likelihood of labeled data and a generative likelihood of unlabeled data. While prominent semi-supervised learning methods assume low density regions between classes or are subject to generative modeling assumptions, hybrid generative/discriminative methods allow semi-supervised learning in the presence of strongly overlapping classes and reduce the risk of modeling structure in the unlabeled data that is irrelevant for the specific …
Generalized Component Analysis For Text With Heterogeneous Attributes, Xuerui Wang, Chris Pal, Andrew Mccallum
Generalized Component Analysis For Text With Heterogeneous Attributes, Xuerui Wang, Chris Pal, Andrew Mccallum
Andrew McCallum
We present a class of richly structured, undirected hidden variable models suitable for simultaneously modeling text along with other attributes encoded in different modalities. Our model generalizes techniques such as Principal Component Analysis to heterogeneous data types. In contrast to other approaches, this framework allows modalities such as words, authors and timestamps to be captured in their natural, probabilistic encodings. We demonstrate the effectiveness of our framework on the task of author prediction from 13 years of the NIPS conference proceedings and for a recipient prediction task using a 10-month academic email archive of a researcher. Our approach should be …