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» Learning from Highly Structured Data by Decomposition
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EMNLP
2006
14 years 11 months ago
Domain Adaptation with Structural Correspondence Learning
Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For...
John Blitzer, Ryan T. McDonald, Fernando Pereira
EDBT
2006
ACM
266views Database» more  EDBT 2006»
15 years 10 months ago
From Analysis to Interactive Exploration: Building Visual Hierarchies from OLAP Cubes
We present a novel framework for comprehensive exploration of OLAP data by means of user-defined dynamic hierarchical visualizations. The multidimensional data model behind the OLA...
Svetlana Vinnik, Florian Mansmann
ICML
2008
IEEE
15 years 10 months ago
Structure compilation: trading structure for features
Structured models often achieve excellent performance but can be slow at test time. We investigate structure compilation, where we replace structure with features, which are often...
Dan Klein, Hal Daumé III, Percy Liang
ECAI
2004
Springer
15 years 3 months ago
Towards Efficient Learning of Neural Network Ensembles from Arbitrarily Large Datasets
Advances in data collection technologies allow accumulation of large and high dimensional datasets and provide opportunities for learning high quality classification and regression...
Kang Peng, Zoran Obradovic, Slobodan Vucetic
TIT
2008
134views more  TIT 2008»
14 years 9 months ago
Communication Over MIMO X Channels: Interference Alignment, Decomposition, and Performance Analysis
In a multiple-antenna system with two transmitters and two receivers, a scenario of data communication, known as the X channel, is studied in which each receiver receives data from...
Mohammad Ali Maddah-Ali, Abolfazl S. Motahari, Ami...