By Edmondo Trentin (auth.), Friedhelm Schwenker, Simone Marinai (eds.)
This publication constitutes the refereed complaints of the second one IAPR Workshop on synthetic Neural Networks in trend popularity, ANNPR 2006, held in Ulm, Germany in August/September 2006.
The 26 revised papers offered have been conscientiously reviewed and chosen from forty nine submissions. The papers are equipped in topical sections on unsupervised studying, semi-supervised studying, supervised studying, help vector studying, a number of classifier structures, visible item acceptance, and information 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
Using spiking neural network models aims towards an understanding of how pattern recognition problems could be solved in the brain. If a mechanism can not be implemented with biologically realistic spiking neurons, then it is unlikely that this mechanism is used in the brain. Furthermore spiking neurons provide for high temporal precision, which is relevant for real-world applications. g. for spatio-temporal pattern recognition or for audio patterns. 1 Network Architecture The network is organized in two layers of spiking neurons: the input layer U0 and the representation layer U1 (Fig.
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. When a spike occurs, the pre- or postsynaptic learning potentials Lpre,m or Lpost,n are increased by 1. They exponentially decrease with time constant τpre = 20ms and τpost = 10ms. R is a constant corresponding to the learning rate and was tuned to allow for a weight change between 5 and 20 % after 10 stimulus presentations.
SOM is less ﬂexible than NG, since the lattice topology need not ﬁt the data topology. g. using a two-dimensional regular lattice. The original SOM does not possess a cost function. A slight variation of SOM proposed by Heskes  has the cost function ESOM (W ) = 1 2C(λ) n i=1 χ∗i (v, W ) · n hλ (nd(i, l)) · (v − wl )2 P (v)dv l=1 where C(λ) is again a constant, nd(i, j) denotes the neighborhood range of neuron i and j on the priorly ﬁxed lattice, and χ∗i (v, W ) is one for neuron i iﬀ the n average l=1 hλ (nd(i, l)) · (v − wi )2 is minimum, otherwise it is zero.