Mass Estimation and Its Applications

Abstract

This project established that the new modeling mechanism--mass estimation--has a strong theoretical underpinning for prediction and data modeling. It showed that mass-based approaches have time and space complexities more favorable than existing approaches in a number of data mining tasks e.g., anomaly detection, clustering and information retrieval, and developed (i) a new density estimator based on mass, and (ii) a new generative classifier based on mass. The results have been published in top conferences and accepted for top journals.

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Document Details

Document Type
Technical Report
Publication Date
Feb 23, 2012
Accession Number
ADA556329

Entities

People

  • Kai Ming Ting

Organizations

  • Monash University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Anomaly Detection
  • Bayesian Networks
  • Change Detection
  • Data Mining
  • Data Science
  • Databases
  • Detectors
  • Information Processing
  • Information Retrieval
  • Information Science
  • Information Systems
  • Kernel Functions
  • Machine Learning
  • Network Science
  • Statistical Algorithms
  • Supervised Machine Learning
  • Three Dimensional

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Groundwater Contamination Remediation.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space