Can Two Hidden Layers Make a Difference?

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F13%3A00391257" target="_blank" >RIV/67985807:_____/13:00391257 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-37213-1_4" target="_blank" >http://dx.doi.org/10.1007/978-3-642-37213-1_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-37213-1_4" target="_blank" >10.1007/978-3-642-37213-1_4</a>

Result language
angličtina
Original language name
Can Two Hidden Layers Make a Difference?
Original language description
Representations of multivariable Boolean functions by one and two-hidden-layer Heaviside perceptron networks are investigated. Sufficient conditions are given for representations with the numbers of network units depending on the input dimension d linearly and polynomially. Functions with such numbers depending on d exponentially or having some weights exponentially large are described in terms of properties of their communication matrices. A mathematical formalization of the concept of ?highly-varyingfunctions is proposed. There is given an example of such function which can be represented by a network with two hidden layers with merely d units.
Czech name
—
Czech description
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Project
<a href="/en/project/GAP202%2F11%2F1368" target="_blank" >GAP202/11/1368: Learning of functional relationships from high-dimensional data</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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