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114
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ICML
2000
IEEE
16 years 1 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
107
Voted
WWW
2008
ACM
16 years 1 months ago
Efficiently finding web services using a clustering semantic approach
Efficiently finding Web services on the Web is a challenging issue in service-oriented computing. Currently, UDDI is a standard for publishing and discovery of Web services, and U...
Jiangang Ma, Yanchun Zhang, Jing He
105
Voted
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
16 years 26 days ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney
ICIAP
2005
ACM
16 years 18 days ago
A Neural Adaptive Algorithm for Feature Selection and Classification of High Dimensionality Data
In this paper, we propose a novel method which involves neural adaptive techniques for identifying salient features and for classifying high dimensionality data. In particular a ne...
Elisabetta Binaghi, Ignazio Gallo, Mirco Boschetti...
92
Voted
CVPR
2010
IEEE
15 years 8 months ago
Automatic Discovery of Meaningful Object Parts with Latent CRFs
Object recognition is challenging due to high intra-class variability caused, e.g., by articulation, viewpoint changes, and partial occlusion. Successful methods need to strike a...
Paul Schnitzspan, Stefan Roth, Bernt Schiele