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NIPS
1994
15 years 7 months ago
Efficient Methods for Dealing with Missing Data in Supervised Learning
We present efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation ...
Volker Tresp, Ralph Neuneier, Subutai Ahmad
ANLP
1992
116views more  ANLP 1992»
15 years 7 months ago
Automatic Learning for Semantic Collocation
The real di culty in development of practical NLP systems comes from the fact that we do not have e ective means for gathering \knowledge". In this paper, we propose an algor...
Satoshi Sekine, Jeremy J. Carroll, Sophia Ananiado...
ACL
2010
15 years 4 months ago
Hierarchical Sequential Learning for Extracting Opinions and Their Attributes
Automatic opinion recognition involves a number of related tasks, such as identifying the boundaries of opinion expression, determining their polarity, and determining their inten...
Yejin Choi, Claire Cardie
ICML
2005
IEEE
16 years 7 months ago
Predictive low-rank decomposition for kernel methods
Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These decompositions have generally been treated as black...
Francis R. Bach, Michael I. Jordan
ICML
2005
IEEE
16 years 7 months ago
Robust one-class clustering using hybrid global and local search
Unsupervised learning methods often involve summarizing the data using a small number of parameters. In certain domains, only a small subset of the available data is relevant for ...
Gunjan Gupta, Joydeep Ghosh