By Edmondo Trentin (auth.), Friedhelm Schwenker, Simone Marinai (eds.)
This e-book constitutes the refereed lawsuits of the second one IAPR Workshop on synthetic Neural Networks in development acceptance, ANNPR 2006, held in Ulm, Germany in August/September 2006.
The 26 revised papers awarded have been rigorously reviewed and chosen from forty nine submissions. The papers are geared up in topical sections on unsupervised studying, semi-supervised studying, supervised studying, aid vector studying, a number of classifier structures, visible item popularity, and knowledge mining in bioinformatics.
Read Online or Download Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006. Proceedings PDF
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Extra resources for Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006. Proceedings
A local optimum of the latter is also a ˆ has been found in local optimum of the ﬁrst one. Thus, a local optimum of E this case. Supervised Batch Neural Gas 5 43 Future Approaches This general formulation opens the way towards a couple of concrete algorithms for diﬀerent application areas. We shortly mention a few possibilities which seem particularly promising and which experimental investigation will be the subject of future research. 1 Supervised Batch SOM As already mentioned, the general formulation of batch optimization includes the possibility to extend SOM by a supervised component.
B: Spikes of representation layer U1 . c: Membrane potential V (t) of neuron #0 of U1 (gray line in b). pkm ⎧ ⎨ 1 , m ≤ no = 1 , no + nu (m − 1) < m ≤ no + nu m ⎩ 0 , otherwise . (19) na = fa NU0 is the number and fa the fraction of active neurons in each pattern. no = fo na is the number of neurons which are active in each pattern (overlap) and nu = na − no is the number of neurons which are unique for each pattern. 5 Performance Measure In order to quantify the ability of the network to discriminate between the stimuli, we simulated a test phase after every learning phase.
Ei is the reverse potential for the inhibitory current which was chosen to be 10 mV lower then the resting potential. 3 Learning Rules The synaptic weight wm,n of the connection from presynaptic U0 neuron m to postsynaptic U1 neuron n is adapted according to a Hebbian learning rule: d wm,n = δn (t)RLpre,m Lpost,n , dt Lpre,m = e sm − t−t τpre (9) , (10) . (11) tsm Lpost,n = e sn − t−t τ post tsn δn (t) is 1 when a spike occurs in the postsynaptic neuron n. tsm and tsn denote the times of the past pre- and postsynaptic spikes.
Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006. Proceedings by Edmondo Trentin (auth.), Friedhelm Schwenker, Simone Marinai (eds.)