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The output weight β is now responsible for learning transformation from the feature space to input data, and it can be determined analytically as ELM with the similar form: boldsymbol{beta} ={left({mathbf{H}}^{mathrm{T}}mathbf{H}+frac{mathbf{I}}{mathbf{C}}right)}^{-1}{mathbf{H}}^{mathrm{T}}mathbf{X} (11).
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The objective function contains a regularizer term and a cost function term that is applied to each pair of cross-domain instances and the learned transformation matrix.
It is then shown how this dynamic of 'perceptual cycles' (Miconi and VanRullen 2010) may be used to learn transformation-invariant representations in the output layer.
The aim of this work is to investigate how such higher layers may exploit the input layer dynamics formed from prior learning about categories in order to segment a visual scene composed of multiple stimuli and learn transformation-invariant representations of them as they move in lockstep across the input layer.
Leading and Learning in Transformation: Exploring the Relationship Between Team Development and School Reform Plan Implementation in Rhode Island.
In this context, we have developed a testbed web tool which aims to be useful for learning model transformation techniques.
We explored the capability of deep neural network in learning geometric transformation and found the model are sensitive to the text image without explicit supervised segmentation information.
An objective function is first defined for learning the transformation matrix.
It is based on a KCCA and consists of learning the transformation between frontal and profile faces.
The proposed approach for face identification relies on the idea of learning a transformation which maps the profile faces onto their corresponding frontal faces using canonical correlation analysis (CCA).
But resilience is not just a simple "bouncing back" to the status quo that systems accomplish after certain events, but also comprises a multidimensional "bounce forward" (Manyena 2009, p. 261) that enables learning and transformation.
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