• 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
Publication January 1, 2008

On Updates That Constrain the Features’ Connections During Learning

Citation

Copy to clipboard


KDD ’08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2008 Pages 515–523https://doi.org/10.1145/1401890.1401954

Abstract

In many multi-class learning scenarios, the number of classes is relatively large (thousands,…), or the space and time efficiency of the learning system can be crucial. We investigate two online update techniques especially suited to such problems. These updates share a sparsity preservation capacity: they allow for constraining the number of prediction connections that each feature can make. We show that one method, exponential moving average, is solving a “discrete” regression problem for each feature, changing the weights in the direction of minimizing the quadratic loss. We design the other method to improve a hinge loss subject to constraints, for better accuracy. We empirically explore the methods, and compare performance to previous indexing techniques, developed with the same goals, as well as other online algorithms based on prototype learning. We observe that while the classification accuracies are very promising, improving over previous indexing techniques, the scalability benefits are preserved.

↓ 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