Intelligent Learning

Abstract

The report considers general machine learning models, where knowledge transfer is positioned as the main method to improve their convergence properties. Previous research was focused on mechanisms of knowledge transfer in the context of SVM framework; the report shows that this mechanism is applicable to neural network framework as well. The report describes several general approaches for knowledge transfer in both SVM and ANN frameworks and illustrates algorithmic implementations and performance of one of these approaches for several synthetic examples.

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Document Details

Document Type
Technical Report
Publication Date
Apr 16, 2019
Accession Number
AD1085890

Entities

People

  • Vladimir Vapnik

Organizations

  • Trustees of Columbia University in the City of New York

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Data Analysis
  • Data Mining
  • Guarantees
  • Indicators
  • Information Theory
  • Kernel Functions
  • Language
  • Machine Learning
  • Neural Networks
  • New York
  • Probability
  • Scientific Research
  • Standards
  • Supervised Machine Learning
  • Training

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks