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» Learning Object Representations Using Sequential Patterns
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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...
JMLR
2002
133views more  JMLR 2002»
13 years 7 months ago
Learning Precise Timing with LSTM Recurrent Networks
The temporal distance between events conveys information essential for numerous sequential tasks such as motor control and rhythm detection. While Hidden Markov Models tend to ign...
Felix A. Gers, Nicol N. Schraudolph, Jürgen S...
AAAI
2004
13 years 9 months ago
Rapid Object Recognition from Discriminative Regions of Interest
Object recognition and detection represent a relevant component in cognitive computer vision systems, such as in robot vision, intelligent video surveillance systems, or multi-mod...
Gerald Fritz, Christin Seifert, Lucas Paletta, Hor...
VMCAI
2010
Springer
14 years 4 months ago
Considerate Reasoning and the Composite Design Pattern
We propose Considerate Reasoning, a novel specification and verification technique based on object invariants. This technique supports succinct specifications of implementations wh...
Alexander J. Summers, Sophia Drossopoulou
ICDAR
1997
IEEE
13 years 12 months ago
Modeling Documents for Structure Recognition Using Generalized N-Grams
In this paper we present and discuss a novel approach to modeling logical structures of documents, based on a statistical representation of patterns in a document class. An effic...
Rolf Brugger, Abdel Wahab Zramdini, Rolf Ingold