A Cognitive Study of Learning with Labeled and Unlabeled Data

Abstract

In semi-supervised learning (SSL) the learner is presented with both labeled and unlabeled data. If the learner makes certain assumptions regarding the distribution of the unlabeled items p(x) and the class conditional p(y | x), they can learn the concept faster and more accurately. We investigate how humans are affected by unlabeled data in a supervised categorization task. Our project lead to better understanding of human learning, improvements in human teaching strategy, improvements in human/machine cooperative learning and improvements in machine learning models. Our empirical evidence for human SSL includes several human behavioral studies that definitively show the influence of unlabeled data in human category learning. Our theoretical models produce plausible semi-supervised learning models for human learning and machine learning. We utilize these observations to enhance human learning.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2012
Accession Number
ADA565197

Entities

People

  • Timothy T. Rogers
  • Xiaojin Zhu

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Bayesian Networks
  • Boundaries
  • Classification
  • Cognitive Science
  • Educational Psychology
  • Learning
  • Machine Learning
  • Models
  • Psychology
  • Semi-Supervised Learning
  • Supervised Machine Learning
  • Training
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks