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10 601 (0,141s)

Result

Classification by Sparse Neural Networks

of computational units. Bounds on these norms are proven using the ChernoffHoeffding bound...

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

  • 2019
  • Jimp
  • Link
Result

Probabilistic Lower Bounds for Approximation by Shallow Perceptron Networks

Limitations of approximation capabilities of shallow perceptron networks are investigated. Lower bounds on approximation errors are derived for binary-valued functions on a given domain. The results are obtained by combining probabilistic

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

  • 2017
  • Jimp
  • Link
Result

Probabilistic Bounds on Complexity of Networks Computing Binary Classification Tasks

of computational units. Probabilistic lower bounds on these norms are derived using the Chernoff-Hoeffding Bound on sums of independent random variables, which need......

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

  • 2018
  • D
  • Link
Result

A tail bound for read-k families of functions

We prove a Chernoff-like large deviation bound on the sum of read-k non-independent random variables.

BA - Obecná matematika

  • 2015
  • Jx
  • Link
Result

Hoeffding and Bernstein Inequalities for U-statistics without Replacement

Concentration inequalities quantify random fluctuations of functions of random variables, typically by bounding the probability that such a function differs from its expected value by more than a certain amount. In this paper, we extend ...

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

  • 2022
  • Jimp
  • Link
Result

Hoeffding’s Inequality for Sums of Dependent Random Variables

is a sharp upper bound on the probability that their sum is significantly larger than their mean? In the case of independent random variables, a fundamental tool for bounding such probabilities is devised by Wassily Hoeffding

Pure mathematics

  • 2017
  • Jimp
  • Link
Result

Hoeffding-Serfling Inequality for U-Statistics Without Replacement

Concentration inequalities quantify random fluctuations of functions of random variables, typically by bounding the probability that such a function differs from its expected value by more than a certain amount. In this paper we study one pa...

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

  • 2022
  • O
  • Link
Result

Hoeffding-Serfling Inequality for U-Statistics Without Replacement

Concentration inequalities quantify random fluctuations of functions of random variables, typically by bounding the probability that such a function differs from its expected value by more than a certain amount. In this paper we study one pa...

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

  • 2023
  • Jimp
  • Link
Result

Model Complexities of Shallow Networks Representing Highly Varying Functions

of variational norms combined with the probabilistic Chernoff Bound. It is shown that almostModel complexities of shallow (i.e., one-hidden-layer) networks representing highly varying multivariable {-1,1}{-1,1}-valued functions are...

IN - Informatika

  • 2016
  • Jx
  • Link
Result

Probabilistic Bounds for Binary Classification of Large Data Sets

A probabilistic model for classification of task relevance is investigated. Correlations between randomly-chosen functions and network input-output functions are estimated. Impact of large data sets is analyzed from the point of view of the concentra...

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

  • 2020
  • D
  • Link
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