PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System.

Abstract

Most artificial intelligence systems today work on simple problems and artificial domains because they rely on the accurate sensing of the task world. Object recognition is a crucial part of the sensing challenge and machine learning stands in a position to catapult object recognition into real world domains. Given that, to date, machine learning has not delivered general object recognition, we propose a different point of attack: the learning architectures themselves. We have developed a method for directly learning and combining algorithms in a new way that imposes little burden on or bias from the humans involved. This learning architecture, PADO, and the new results it brings to the problem of natural image object recognition is the focus of this report.

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Document Details

Document Type
Technical Report
Publication Date
Feb 10, 1995
Accession Number
ADA293109

Entities

People

  • Astro Teller
  • Manuela M. Veloso

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Computations
  • Computer Languages
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computer Vision
  • Computers
  • Electrical Engineering
  • Genetic Algorithms
  • Language
  • Machine Learning
  • Neural Networks
  • Object Recognition

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computer Vision.
  • Strategic Security Studies

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks