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NIPS
2004
13 years 11 months ago
Semi-Markov Conditional Random Fields for Information Extraction
We describe semi-Markov conditional random fields (semi-CRFs), a conditionally trained version of semi-Markov chains. Intuitively, a semiCRF on an input sequence x outputs a "...
Sunita Sarawagi, William W. Cohen
ICPR
2000
IEEE
14 years 11 months ago
Image Distance Using Hidden Markov Models
We describe a method for learning statistical models of images using a second-order hidden Markov mesh model. First, an image can be segmented in a way that best matches its stati...
Daniel DeMenthon, David S. Doermann, Marc Vuilleum...
MICCAI
2010
Springer
13 years 8 months ago
Automatic Detection and Segmentation of Axillary Lymph Nodes
Lymph node detection and measurement is a difficult and important part of cancer treatment. In this paper we present a robust and effective learning-based method for the automatic...
Adrian Barbu, Michael Sühling, Xun Xu, David ...
EMNLP
2008
13 years 11 months ago
An Analysis of Active Learning Strategies for Sequence Labeling Tasks
Active learning is well-suited to many problems in natural language processing, where unlabeled data may be abundant but annotation is slow and expensive. This paper aims to shed ...
Burr Settles, Mark Craven
ICRA
2008
IEEE
191views Robotics» more  ICRA 2008»
14 years 4 months ago
Combining automated on-line segmentation and incremental clustering for whole body motions
Abstract— This paper describes a novel approach for incremental learning of human motion pattern primitives through on-line observation of human motion. The observed motion time ...
Dana Kulic, Wataru Takano, Yoshihiko Nakamura