All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Constructive Lower Bounds on Model Complexity of Shallow Perceptron Networks

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00474092" target="_blank" >RIV/67985807:_____/18:00474092 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/s00521-017-2965-0" target="_blank" >http://dx.doi.org/10.1007/s00521-017-2965-0</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s00521-017-2965-0" target="_blank" >10.1007/s00521-017-2965-0</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Constructive Lower Bounds on Model Complexity of Shallow Perceptron Networks

  • Original language description

    Limitations of shallow (one-hidden-layer) perceptron networks are investigated with respect to computing multivariable functions on finite domains. Lower bounds are derived on growth of the number of network units or sizes of output weights in terms of variations of functions to be computed. A concrete construction is presented with a class of functions which cannot be computed by signum or Heaviside perceptron networks with considerably smaller numbers of units and smaller output weights than the sizes of the function’s domains. A subclass of these functions is described whose elements can be computed by two-hidden-layer perceptron networks with the number of units depending on logarithm of the size of the domain linearly.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA15-18108S" target="_blank" >GA15-18108S: Model complexity of neural, radial, and kernel networks</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Neural Computing & Applications

  • ISSN

    0941-0643

  • e-ISSN

  • Volume of the periodical

    29

  • Issue of the periodical within the volume

    7

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    11

  • Pages from-to

    305-315

  • UT code for WoS article

    000427799400002

  • EID of the result in the Scopus database

    2-s2.0-85018255699