By Toshinori Deguchi, Junya Fukuta, Naohiro Ishii (auth.), Marco Tomassini, Alberto Antonioni, Fabio Daolio, Pierre Buesser (eds.)
The e-book constitutes the refereed court cases of the eleventh foreign convention on Adaptive and usual Computing Algorithms, ICANNGA 2013, held in Lausanne, Switzerland, in April 2013.
The fifty one revised complete papers provided have been conscientiously reviewed and chosen from a complete of ninety one submissions. The papers are prepared in topical sections on neural networks, evolutionary computation, smooth computing, bioinformatics and computational biology, complicated computing, and applications.
Read Online or Download Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings PDF
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Additional info for Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings
Section 3 investigates model complexities of one and two-hiddenlayer perceptron networks representing d-variable Boolean functions. Section 4 proposes a mathematical formalization of the concept of a “highly-varying function” in terms of a variational norm with respect to a type of network units. Section 5 is a discussion. 2 Preliminaries A widely-used network architecture is a one-hidden-layer network with a single linear output. Such a network with n hidden units can compute input-output functions from the set n wi gi wi ∈ R, gi ∈ G , spann G := i=1 where G, called dictionary, is a set of functions computable by a given type of units.
The author utilized a similar assertion justifying the usage of asymmetric dissimilarities. Also, in , where the improved version of the asymmetric k-means algorithm was proposed, the asymmetric dissimilarity was employed as preferable over the standard symmetric one. , the most popular Euclidean distance) do not grasp to this phenomenon, and for most pairs of data points, they produce small values (similarities) or big values (dissimilarities). Consequently, they do not reﬂect accurately the relationships between objects.
Rowan and S. 6 Fig. 2. E activity during pruning with (blue / upper) and without (green / lower) compensatory synaptic scaling. Run time 160,000 s (≈ 44 h). 5 (b) Scale factors of E cells Fig. 3. 2 Synaptic Scaling Did Not Disrupt Network Behavior The model was run for 160,000 s (≈ 44 h) to examine the eﬀects of scaling over time on network dynamics. With scaling, activity of the E cells remained steady (Fig. 3a), and scale factors remained centered around 1 (Fig. 3b). Scaling appeared to preserve stability of the network during these extremely long runs.