• Skip to primary navigation
  • Skip to main content
SRI logo
  • About
    • Press room
    • Our history
  • Expertise
    • Advanced imaging systems
    • Artificial intelligence
    • Biomedical R&D services
    • Biomedical sciences
    • Computer vision
    • Cyber & formal methods
    • Education and learning
    • Innovation strategy and policy
    • National security
    • Ocean & space
    • Quantum
    • QED-C
    • Robotics, sensors & devices
    • Speech & natural language
    • Video test & measurement
  • Ventures
  • NSIC
  • Careers
  • Contact
  • 日本支社
Search
Close
Cyber & formal methods publications January 1, 2008

Large-Scale Many-Class Learning

Citation

Copy to clipboard


Large-Scale Many-Class Learning Omid Madani and Michael Connor Proceedings of the 2008 SIAM International Conference on Data Mining (SDM). 2008, 846-857

Abstract

A number of tasks, such as large-scale text categorization and word prediction, can benefit from efficient learning and classification when the number of classes (categories), in addition to instances and features, is large, that is, in the thousands and beyond. We investigate learning of sparse category indices to address this challenge. An index is a weighted bipartite graph mapping features to categories. On presentation of an instance, the index retrieves and scores a small set of candidate categories. The candidates can then be ranked and the ranking or the scores can be used for category assignment. We present novel online index learning algorithms. When compared to other approaches, including one-versus-rest and top-down learning and classification using support vector machines, we find that indexing is highly advantageous in terms of space and time efficiency, at both training and classification times, while yielding similar and often better accuracies. On problems with hundreds of thousands of instances and thousands of categories, the index is learned in minutes, while other methods can take orders of magnitude longer. As we explain, the design of the algorithm makes it convenient to maintain a constraint on the number of prediction connections a feature is allowed to make. This constraint is crucial in yielding efficient learning and classification.

↓ View online

Share this

How can we help?

Once you hit send…

We’ll match your inquiry to the person who can best help you.

Expect a response within 48 hours.

Career call to action image

Make your own mark.

Search jobs

Our work

Case studies

Publications

Timeline of innovation

Areas of expertise

Institute

Leadership

Press room

Media inquiries

Compliance

Careers

Job listings

Contact

SRI Ventures

Our locations

Headquarters

333 Ravenswood Ave
Menlo Park, CA 94025 USA

+1 (650) 859-2000

Subscribe to our newsletter


日本支社
SRI International
  • Contact us
  • Privacy Policy
  • Cookies
  • DMCA
  • Copyright © 2022 SRI International