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GBRPR
2009
Springer
16 years 2 months ago
A Graph Based Data Model for Graphics Interpretation
A universal data model, named DG, is introduced to handle vectorized data uniformly during the whole recognition process. The model supports low level graph algorithms as well as h...
Endre Katona
ISCAS
2005
IEEE
154views Hardware» more  ISCAS 2005»
16 years 1 months ago
Back propagation learning of neural networks with chaotically-selected affordable neurons
— Cell assembly is one of explanations of information processing in the brain, in which an information is represented by a firing space pattern of a group of plural neurons. On ...
Yoko Uwate, Yoshifumi Nishio
UML
2005
Springer
16 years 1 months ago
Practical Declarative Model Transformation with Tefkat
We present Tefkat, an implementation of a language designed specically for the transformation of MOF models using patterns and rules. The language adopts a declarative paradigm, w...
Michael Lawley, Jim Steel
KDD
1997
ACM
109views Data Mining» more  KDD 1997»
15 years 11 months ago
Beyond Concise and Colorful: Learning Intelligible Rules
A variety of techniques from statistics, signal processing, pattern recognition, machine learning, and neural networks have been proposed to understand data by discovering useful ...
Michael J. Pazzani, Subramani Mani, William Rodman...
ICGI
2000
Springer
15 years 11 months ago
Computation of Substring Probabilities in Stochastic Grammars
Abstract. The computation of the probability of string generation according to stochastic grammars given only some of the symbols that compose it underlies pattern recognition prob...
Ana L. N. Fred