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» Optimal feature selection for support vector machines
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SSPR
2010
Springer
13 years 6 months ago
Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models
Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial d...
André F. T. Martins, Manuele Bicego, Vittor...
TNN
1998
125views more  TNN 1998»
13 years 7 months ago
Symbolic connectionism in natural language disambiguation
Abstract—Natural language understanding involves the simultaneous consideration of a large number of different sources of information. Traditional methods employed in language an...
Samuel W. K. Chan, James Franklin
OSDI
2008
ACM
14 years 8 months ago
Experiences with Content Addressable Storage and Virtual Disks
Efficiently managing storage is important for virtualized computing environments. Its importance is magnified by developments such as cloud computing which consolidate many thousa...
Anthony Liguori, Eric Van Hensbergen
NECO
2007
115views more  NECO 2007»
13 years 7 months ago
Training Recurrent Networks by Evolino
In recent years, gradient-based LSTM recurrent neural networks (RNNs) solved many previously RNN-unlearnable tasks. Sometimes, however, gradient information is of little use for t...
Jürgen Schmidhuber, Daan Wierstra, Matteo Gag...
ICDM
2006
IEEE
119views Data Mining» more  ICDM 2006»
14 years 1 months ago
Fast On-line Kernel Learning for Trees
Kernel methods have been shown to be very effective for applications requiring the modeling of structured objects. However kernels for structures usually are too computational dem...
Fabio Aiolli, Giovanni Da San Martino, Alessandro ...