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This classification problem exemplifies the differences between the "hard" and "soft" learning strategies.

Figure 10 demonstrates the good agreement between the analytical solutions for the ensemble- averaged m u distribution, population average GFP response, and the ensemble average classifier performance and the simulation results for "soft" learning with γ = 1.

This analytical description is exact in the case of infinitely "soft" learning and infinite number of cells.

For instance, during the early stage of soft learning (i.e. active cluster merging), the algorithm could be applied to small 'chunks' of chromosomes.

Analytical treatment of soft learning; performance of the classifier model on the problem of discriminating toxicity data; classifier model based on experimentally derived PRM promoter response function; effect of non-uniformity of m u distribution in "master population".

We have developed an analytical theory that describes the training and the resulting performance of the cell-based classifier in the limit of "soft" learning (see Supporting Information Section 1).

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Similar(51)

Because there is evidence of VPs promoting helpful cognitive preparation for soft skills learning and the physical examination [ 20- 22], it is surprising that no data are available on the use of VPs as preparation for procedural skills training.

This paper presents a batch competitive learning method called fuzzy-soft learning vector quantization (FSLVQ).

An affine T-S fuzzy system is derived naturally from linearizing a nonlinear system or from some data-driven identification techniques, for example, cluster-based algorithms or soft-computation learning structures.

Igoe argued that the soft skills learned in FE were more important.

The soft skills learned in the transitional jobs may make participants more appealing to employers by demonstrating that the individuals were able to show up to work on time and could perform satisfactorily in the program jobs.

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