2012 PracticalCollapsedVariationalBa: Difference between revisions
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* ([[2012_PracticalCollapsedVariationalBa|Sato et al., 2012]]) ⇒ [[author::Issei Sato]], [[author::Kenichi Kurihara]], and [[author::Hiroshi Nakagawa]]. ([[year::2012]]). “Practical Collapsed Variational Bayes Inference for Hierarchical Dirichlet Process.” In: [[proceedings::Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining]] ([[conference::KDD-2012]]). ISBN:978-1-4503-1462-6 [http://dx.doi.org/10.1145/2339530.2339550 doi:10.1145/2339530.2339550] | * ([[2012_PracticalCollapsedVariationalBa|Sato et al., 2012]]) ⇒ [[author::Issei Sato]], [[author::Kenichi Kurihara]], and [[author::Hiroshi Nakagawa]]. ([[year::2012]]). “Practical Collapsed Variational Bayes Inference for Hierarchical Dirichlet Process.” In: [[proceedings::Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining]] ([[conference::KDD-2012]]). ISBN:978-1-4503-1462-6 [http://dx.doi.org/10.1145/2339530.2339550 doi:10.1145/2339530.2339550] | ||
<B>Subject Headings:</B> | <B>Subject Headings:</B> | ||
== Notes == | == Notes == | ||
Latest revision as of 19:33, 20 December 2023
- (Sato et al., 2012) ⇒ Issei Sato, Kenichi Kurihara, and Hiroshi Nakagawa. (2012). “Practical Collapsed Variational Bayes Inference for Hierarchical Dirichlet Process.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339550
Subject Headings:
Notes
Cited By
- http://scholar.google.com/scholar?q=%222012%22+Practical+Collapsed+Variational+Bayes+Inference+for+Hierarchical+Dirichlet+Process
- http://dl.acm.org/citation.cfm?id=2339530.2339550&preflayout=flat#citedby
Quotes
Author Keywords
- Collapsed variational bayes inference; [[hierarchical dirichlet process; latent dirichlet allocation; nonparametric bayes; nonparametric statistics
Abstract
We propose a novel collapsed variational Bayes (CVB) inference for the hierarchical Dirichlet process (HDP). While the existing CVB inference for the HDP variant of latent Dirichlet allocation (LDA) is more complicated and harder to implement than that for LDA, the proposed algorithm is simple to implement, does not require variance counts to be maintained, does not need to set hyper-parameters, and has good predictive performance.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2012 PracticalCollapsedVariationalBa | Hiroshi Nakagawa Issei Sato Kenichi Kurihara | Practical Collapsed Variational Bayes Inference for Hierarchical Dirichlet Process | 10.1145/2339530.2339550 | 2012 |