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ECCV
2008
Springer
16 years 6 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 11 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 11 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»
15 years 4 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...
ACL
2011
14 years 8 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