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465 036 (0,517s)

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Generalization in Learning from Examples

It is shown that learning from examples with generalization can be modeled as a regularized inverse problem.

IN - Informatika

  • 2007
  • C
Result

Learning from Examples. The Human and the Non-Human

) are intrinsically connected. The elusive nature of learning from examples – the difficulty of deciding whether learning has taken place – is illustrated using the example, etc.) that only with time assum...

Philosophy, History and Philosophy of science and technology

  • 2023
  • Jost
  • Link
Result

Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses

We study reducibility of learning examples in the learning from entailment learned hypotheses are restricted to have bounded treewidth. We show that in such cases thereis a polynomial-time reduction algori...

JC - Počítačový hardware a software

  • 2013
  • D
  • Link
Result

Statistical formulation of structured output learning from partially annotated examples

for learning from partially annotated examples, two crucial problems remain open: 1) an exact statistical formulation of risk minimization based learning from partially annotated examples and 2) ...

JD - Využití počítačů, robotika a její aplikace

  • 2013
  • D
  • Link
Result

Interval Insensitive Loss for Ordinal Classification

classifiers learned from partially annotated examples can achieve accuracy close to the accuracyof classifiers learned from completely annotated examples.We address a problem of learning...

JD - Využití počítačů, robotika a její aplikace

  • 2014
  • D
Result

Graph convolutional networks for learning with few clean and many noisy labels

as a binary classifier, which learns to discriminate clean from noisy examples usingIn this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples

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

  • 2020
  • D
  • Link
Result

Comparison of Kernel Based Regularization Networks and RBF Networks

We discuss two approaches to the problem of learning from examples. They are RBF networks and regularization networks (RN). Performance of both approaches is demonstrated on experiments. We claim that the performance of RN ...

BA - Obecná matematika

  • 2004
  • D
Result

Learning Maximum Margin Markov Networks from examples with missing labels

Network (MN) classifier can be efficiently learned by the maximum margin method, which however requires expensive completely annotated examples. We extend the maximum margin algorithm for learning of unrestricted MN classi...

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

  • 2021
  • D
  • Link
Result

Supervised Learning with Generalization as an Inverse Problem

Capability of generalization in learning of neural networks from examples can be modelled using regularization, which has been developed as a tool for improving by integraloperators. It is shown that learning f...

BA - Obecná matematika

  • 2005
  • Jx
Result

Learning Objects and some Applications of them

of learning object are introduced. Two examples are demonstrated. One from an European project CELEBRATE, the second from a national project Outcomes, resourcesThe aim of the paper is to show a learning a...

AM - Pedagogika a školství

  • 2005
  • D
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