Learning with Data Adaptive Features

Abstract

It is frequently observed that the dimension of inputs is much larger than the sample size. Examples are image construction, microarray data, data mining etc. In such cases, standard learning methods either are not applicable or perform badly. Also, identifying a small subset of important features, which discriminate outputs, becomes an important subject. Hence, good learning algorithms with high dimensional inputs should provides a classification rule which not only yields high accuracy but also has an ability of identifying few important features. Apparently, there are two popularly used such algorithms. One is decision trees and the other is LASSO. The objective of this research is to develop new algorithms for decision tree and LASSO for improving computational power, prediction accuracy, and ability of detecting significant features.

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Document Details

Document Type
Technical Report
Publication Date
Jul 27, 2006
Accession Number
ADA451670

Entities

People

  • Yongdai Kim

Organizations

  • Seoul National University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Bayesian Networks
  • Classification
  • Complex Systems
  • Computational Science
  • Data Analysis
  • Data Mining
  • Data Sets
  • Heuristic Methods
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Probability
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Educational Psychology
  • Seismology

Technology Areas

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