A Future for Knowledge Acquisition: 8th European Knowledge by Luc Steels, Guus Schreiber, Walter Van de Velde

By Luc Steels, Guus Schreiber, Walter Van de Velde

This quantity contains a range of the foremost papers awarded on the 8th ecu wisdom Acquisition Workshop (EKAW '94), held in Hoegaarden, Belgium in September 1994.
The ebook demonstrates that paintings within the mainstream of information acquisition results in priceless useful effects and places the information acquisition company in a broader theoretical and technological context. The 21 revised complete papers are conscientiously chosen key contributions; they tackle wisdom modelling frameworks, the id of primary elements, method facets, and architectures and purposes. the amount opens with a considerable preface via the quantity editors surveying the contents.

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Additional resources for A Future for Knowledge Acquisition: 8th European Knowledge Acquisition Workshop, EKAW '94 Hoegaarden, Belgium, September 26–29, 1994 Proceedings

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The Initl method orders nodes in a lattice with randomly set initial weight vectors. Such a rough ordering is consequently "smoothed" by the Filter method that filters values of weights in each lattice direction (along lattice "fibers"). The final result of this pre-setting of weight vectors is a lattice that is visually close to the final state of the lattice. Hence, the training (or now better called tuning) can run for extremely short time. To demonstrate the suggested method we show its abilities on a representative example.

In the 2-D plots, we also show the corresponding standard deviations. Standard deviations in the 3-D plots are not shown, but generally are less than 5 % of the mean value. The effect of architectural bias is clearly visible. This is confirmed by comparing NPM performances (for 0 training epochs) in Fig. l(c)-(e) with the FPM performance in Fig. l(a), effectively implementing VLMMs [8]. About 40 codebook vectors in NPMs are sufficient to make full use of the associated RNNs dynamics. Moreover, NPMs corresponding to nontrained networks achieved NNL comparable with NNL given by RNNs after 10 epochs of training (Fig.

This idea of "soft module" is illustrated on Fig. 2. by a "structured plain" structure (dotted lines represent links with small weights). Figure 2: Types of ANN modularity; "structured plain" topology. 2 Passive Versus Active Approach to Modularization Two basic approaches may be used to discover a modular structure of ANN: passive and active. These two approaches can be characterized: Passive: It is applied after the learning of network. Analysis is done to find modular structure. Network is not modified.

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