Article

A loss bound model for on-line stochastic prediction strategies

Author:

Kenji YamanishiAuthors Info & Claims

COLT '91: Proceedings of the fourth annual workshop on Computational learning theory

Pages 290 - 302

Published: 15 August 1991 Publication History

Abstract

No abstract available.

Cited By

View all

Yamanishi KMaass W(1995)Randomized approximate aggregating strategies and their applications to prediction and discriminationProceedings of the eighth annual conference on Computational learning theory10.1145/225298.225308(83-90)Online publication date: 5-Jul-1995
https://dl.acm.org/doi/10.1145/225298.225308
Abe NTakeuchi J(1993)The “lob-pass” problem and an on-line learning model of rational choiceProceedings of the sixth annual conference on Computational learning theory10.1145/168304.168389(422-428)Online publication date: 1-Aug-1993
https://dl.acm.org/doi/10.1145/168304.168389
Cesa-Bianchi NFreund YHelmbold DHaussler DSchapire RWarmuth MKosaraju RJohnson DAggarwal A(1993)How to use expert adviceProceedings of the twenty-fifth annual ACM symposium on Theory of Computing10.1145/167088.167198(382-391)Online publication date: 1-Jun-1993
https://dl.acm.org/doi/10.1145/167088.167198
Show More Cited By

Index Terms

A loss bound model for on-line stochastic prediction strategies

Recommendations

An Extended Stochastic Loss Bound with Moment Generating Function
CMC '10: Proceedings of the 2010 International Conference on Communications and Mobile Computing - Volume 01

In packet based communication networks, loss bound is a significant parameter in Quality of Service(QoS) guarantees, especially in real-time distributed applications. Unfortunately, most approaches presenting loss problem are based on the approximates ...
Stochastic Bounds for Loss Rates
AICT '07: Proceedings of the The Third Advanced International Conference on Telecommunications

We propose to use stochastic comparison method in order to compute packet loss rates in IP routers for MPLS networks. We study an IP buffer with finite capacity B and we consider two kinds of packets. Those who are delay sensitive and those who are loss ...
Loss strategies for competing AIMD flows

We study in this paper two competing AIMD flows that share a common bottleneck link. When congestion occurs, one (or both) flows will suffer a loss that will cause its throughput to decrease by a multiplicative factor. The identity of the flow that will ...

Comments

Information & Contributors

Information

Published In

COLT '91: Proceedings of the fourth annual workshop on Computational learning theory

August 1991

383 pages

ISBN:1558602135

Editors:
Manfred K. Warmuth
Univ. of California, Santa Cruz
,
Leslie G. Valiant
Harvard Univ., Cambridge, MA

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

Publication History

Published: 15 August 1991

Check for updates

Qualifiers

Article

Conference

COLT'91

Sponsor:

SIGAI
Office Naval Research
SIGACT

COLT'91: 4th Annual Workshop on Computational Learning Theory

August 5 - 7, 1991

California, Santa Cruz, USA

Acceptance Rates

Overall Acceptance Rate 35 of 71 submissions, 49%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Yamanishi KMaass W(1995)Randomized approximate aggregating strategies and their applications to prediction and discriminationProceedings of the eighth annual conference on Computational learning theory10.1145/225298.225308(83-90)Online publication date: 5-Jul-1995
https://dl.acm.org/doi/10.1145/225298.225308
Abe NTakeuchi J(1993)The “lob-pass” problem and an on-line learning model of rational choiceProceedings of the sixth annual conference on Computational learning theory10.1145/168304.168389(422-428)Online publication date: 1-Aug-1993
https://dl.acm.org/doi/10.1145/168304.168389
Cesa-Bianchi NFreund YHelmbold DHaussler DSchapire RWarmuth MKosaraju RJohnson DAggarwal A(1993)How to use expert adviceProceedings of the twenty-fifth annual ACM symposium on Theory of Computing10.1145/167088.167198(382-391)Online publication date: 1-Jun-1993
https://dl.acm.org/doi/10.1145/167088.167198
Yamanishi KHaussler D(1992)Probably almost discriminative learningProceedings of the fifth annual workshop on Computational learning theory10.1145/130385.130404(164-171)Online publication date: 1-Jul-1992
https://dl.acm.org/doi/10.1145/130385.130404

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

An Extended Stochastic Loss Bound with Moment Generating Function

Stochastic Bounds for Loss Rates

Loss strategies for competing AIMD flows

Comments

Published In

Sponsors

Publisher

Publication History

Check for updates

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

Abstract

Cited By

Index Terms

Recommendations

An Extended Stochastic Loss Bound with Moment Generating Function

Stochastic Bounds for Loss Rates

Loss strategies for competing AIMD flows

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations