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ICML
2007
IEEE
16 years 5 months ago
On learning linear ranking functions for beam search
Beam search is used to maintain tractability in large search spaces at the expense of completeness and optimality. We study supervised learning of linear ranking functions for con...
Yuehua Xu, Alan Fern
ICML
2006
IEEE
16 years 5 months ago
A statistical approach to rule learning
We present a new, statistical approach to rule learning. Doing so, we address two of the problems inherent in traditional rule learning: The computational hardness of finding rule...
Stefan Kramer, Ulrich Rückert
133
Voted
ASUNAM
2010
IEEE
15 years 5 months ago
Semi-Supervised Classification of Network Data Using Very Few Labels
The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and Pr...
Frank Lin, William W. Cohen
128
Voted
ICPR
2008
IEEE
15 years 10 months ago
Deformable template combining alignable and non-alignable sketches
This paper proposes a hybrid model for deformable template which combines alignable and non-alignable sketches. These sketches are subject to slight or considerable translations i...
Linjie Zhang, Haifeng Gong, Tianfu Wu, Junyu Dong
CSL
2010
Springer
15 years 4 months ago
Active learning and semi-supervised learning for speech recognition: A unified framework using the global entropy reduction maxi
We propose a unified global entropy reduction maximization (GERM) framework for active learning and semi-supervised learning for speech recognition. Active learning aims to select...
Dong Yu, Balakrishnan Varadarajan, Li Deng, Alex A...