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ECCV
2008
Springer
15 years 11 months ago
Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features
Abstract. Recently, impressive results have been reported for the detection of objects in challenging real-world scenes. Interestingly however, the underlying models vary greatly e...
Paul Schnitzspan, Mario Fritz, Bernt Schiele
ICDM
2009
IEEE
160views Data Mining» more  ICDM 2009»
15 years 4 months ago
Fast Online Training of Ramp Loss Support Vector Machines
—A fast online algorithm OnlineSVMR for training Ramp-Loss Support Vector Machines (SVMR s) is proposed. It finds the optimal SVMR for t+1 training examples using SVMR built on t...
Zhuang Wang, Slobodan Vucetic
ICPR
2008
IEEE
15 years 4 months ago
Fast model selection for MaxMinOver-based training of support vector machines
OneClassMaxMinOver (OMMO) is a simple incremental algorithm for one-class support vector classification. We propose several enhancements and heuristics for improving model select...
Fabian Timm, Sascha Klement, Thomas Martinetz
NECO
2007
115views more  NECO 2007»
14 years 9 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...
93
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ACL
2011
14 years 1 months ago
Joint Training of Dependency Parsing Filters through Latent Support Vector Machines
Graph-based dependency parsing can be sped up significantly if implausible arcs are eliminated from the search-space before parsing begins. State-of-the-art methods for arc filt...
Colin Cherry, Shane Bergsma