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KDD
2004
ACM
166views Data Mining» more  KDD 2004»
14 years 11 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
NN
2002
Springer
208views Neural Networks» more  NN 2002»
13 years 10 months ago
A spiking neuron model: applications and learning
This paper presents a biologically-inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the mo...
Chris Christodoulou, Guido Bugmann, Trevor G. Clar...
KDD
2009
ACM
132views Data Mining» more  KDD 2009»
14 years 11 months ago
Learning patterns in the dynamics of biological networks
Our dynamic graph-based relational mining approach has been developed to learn structural patterns in biological networks as they change over time. The analysis of dynamic network...
Chang Hun You, Lawrence B. Holder, Diane J. Cook
BMCBI
2010
150views more  BMCBI 2010»
13 years 11 months ago
AMS 3.0: prediction of post-translational modifications
Background: We present here the recent update of AMS algorithm for identification of post-translational modification (PTM) sites in proteins based only on sequence information, us...
Subhadip Basu, Dariusz Plewczynski
TNN
2010
176views Management» more  TNN 2010»
13 years 5 months ago
On the weight convergence of Elman networks
Abstract--An Elman network (EN) can be viewed as a feedforward (FF) neural network with an additional set of inputs from the context layer (feedback from the hidden layer). Therefo...
Qing Song