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The generalized Bayesian committee machine

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Volker TrespAuthors Info & Claims

KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 130 - 139

https://doi.org/10.1145/347090.347118

Published: 01 August 2000 Publication History

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Index Terms

The generalized Bayesian committee machine
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2000

537 pages

ISBN:1581132336

DOI:10.1145/347090

Chairmen:
Raghu Ramakrishnan
Univ. of Wisconsin
,
Sal Stolfo
Columbia Univ., New York, NY
,
Roberto Bayardo
IBM Almaden Research Center, San Jose, CA
,
Ismail Parsa
Epsilon

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 August 2000

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KDD00

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KDD00: The Second Annual International Conference on Knowledge Discovery in Data

August 20 - 23, 2000

Massachusetts, Boston, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Kishore SThomas TSachdev HKurpad AWebb P(2022)Modeling the potential impacts of improved monthly income on child stunting in India: a subnational geospatial perspectiveBMJ Open10.1136/bmjopen-2021-05509812:4(e055098)Online publication date: 5-Apr-2022
https://doi.org/10.1136/bmjopen-2021-055098
Liu YToth R(2021)Learning Based Model Predictive Control for Quadcopters with Dual Gaussian Process2021 60th IEEE Conference on Decision and Control (CDC)10.1109/CDC45484.2021.9683258(1515-1521)Online publication date: 14-Dec-2021
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Wang WZhou C(2021)A two-layer aggregation model with effective consistency for large-scale Gaussian process regressionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2021.104449106:COnline publication date: 1-Nov-2021
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Ma Liu Shang (2019)A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior SpaceSustainability10.3390/su1118497211:18(4972)Online publication date: 11-Sep-2019
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Guoyan WFomichev AYiran D(2019)Research on Improved Gaussian Smoothing Filters for SLAM ApplicationMekhatronika, Avtomatizatsiya, Upravlenie10.17587/mau.20.756-76420:12(756-764)Online publication date: 6-Dec-2019
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Fan GZhang X(2012)Gaussian Process-based Manifold Learning for Human Motion ModelingIntelligent Data Analysis for Real-Life Applications10.4018/978-1-4666-1806-0.ch015(283-308)Online publication date: 2012
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Chan ADaxiang Dong (2011)Generalized Gaussian process modelsProceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2011.5995688(2681-2688)Online publication date: 20-Jun-2011
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