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» Evolving Artificial Neural Networks that Develop in Time
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AMC
2005
117views more  AMC 2005»
14 years 11 months ago
Teleonomic entropy: measuring the phase-space of end-directed systems
We introduce a novel way of measuring the entropy of a set of values undergoing changes. Such a measure becomes useful when analyzing the temporal development of an algorithm desi...
Alexander Pudmenzky
CSCW
2008
ACM
15 years 1 months ago
Communication networks in geographically distributed software development
In this paper, we seek to shed light on how communication networks in geographically distributed projects evolve in order to address the limits of the modular design strategy. We ...
Marcelo Cataldo, James D. Herbsleb
IJCNN
2008
IEEE
15 years 6 months ago
Support vector machines and dynamic time warping for time series
— Effective use of support vector machines (SVMs) in classification necessitates the appropriate choice of a kernel. Designing problem specific kernels involves the definition...
Steinn Gudmundsson, Thomas Philip Runarsson, Sven ...
BIOADIT
2004
Springer
15 years 3 months ago
Biologically Plausible Speech Recognition with LSTM Neural Nets
Abstract. Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) are local in space and time and closely related to a biological model of memory in the prefrontal cortex. N...
Alex Graves, Douglas Eck, Nicole Beringer, Jü...
ECAI
2000
Springer
15 years 3 months ago
Learning Efficiently with Neural Networks: A Theoretical Comparison between Structured and Flat Representations
Abstract. We are interested in the relationship between learning efficiency and representation in the case of supervised neural networks for pattern classification trained by conti...
Marco Gori, Paolo Frasconi, Alessandro Sperduti