About: Pachinko allocation

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Pachinko allocation Goto Sponge NotDistinct Permalink

An Entity of Type : yago:Whole100003553, within Data Space : dbpedia.demo.openlinksw.com associated with source document(s)
QRcode icon

http://dbpedia.demo.openlinksw.com/c/6pcVRxp6SS

In machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model. Topic models are a suite of algorithms to uncover the hidden thematic structure of a collection of documents. The algorithm improves upon earlier topic models such as latent Dirichlet allocation (LDA) by modeling correlations between topics in addition to the word correlations which constitute topics. PAM provides more flexibility and greater expressive powerthan latent Dirichlet allocation. While first described and implemented in the context of natural language processing, the algorithm may have applications in other fields such as bioinformatics. Themodel is named for pachinko machines—a game popular in Japan, in which metal balls bounce down arounda complex collection of pins unt

Attributes	Values
rdf:type	person yago:WikicatLatentVariableModels yago:Assistant109815790 yago:CausalAgent100007347 yago:LivingThing100004258 yago:Model110324560 yago:Object100002684 yago:Organism100004475 yago:Person100007846 yago:PhysicalEntity100001930 yago:Worker109632518 yago:YagoLegalActor yago:YagoLegalActorGeo yago:Whole100003553
rdfs:label	Pachinko allocation (en) Размещение патинко (ru)
rdfs:comment	Размещение патинко (англ. pachinko allocation, PAM) — метод тематического моделирования, применяемый в машинном обучении и обработке естественного языка, позволяющий обнаружить скрытую тематическую структуру в коллекции документов. От более ранних методов (например, LDA) алгоритм отличается тем, что моделирует корреляции между темами в дополнение к корреляциям слов, задающих темы. PAM превосходит LDA по гибкости и выразительной силе. Впервые метод описан, реализован и применён для обработки текстов на естественном языке, однако, может быть использован и в других областях, например, для задач биоинформатики. Своё название получил благодаря популярным в Японии игровым автоматам патинко, в которых реализована игра, напоминающая пинбол на доске Гальтона. (ru) In machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model. Topic models are a suite of algorithms to uncover the hidden thematic structure of a collection of documents. The algorithm improves upon earlier topic models such as latent Dirichlet allocation (LDA) by modeling correlations between topics in addition to the word correlations which constitute topics. PAM provides more flexibility and greater expressive powerthan latent Dirichlet allocation. While first described and implemented in the context of natural language processing, the algorithm may have applications in other fields such as bioinformatics. Themodel is named for pachinko machines—a game popular in Japan, in which metal balls bounce down arounda complex collection of pins unt (en)
dct:subject	Latent variable models Statistical natural language processing
Wikipage page ID	28082011 (xsd:integer)
Wikipage revision ID	1103041695 (xsd:integer)
Link from a Wikipage to another Wikipage	Natural language processing Andrew McCallum University of Massachusetts Amherst David Blei Andrew Ng Machine learning Mallet (software project) Latent variable models Probabilistic latent semantic indexing Statistical natural language processing Latent Dirichlet allocation Pachinko Bioinformatics Directed acyclic graph Michael I. Jordan Topic model
Link from a Wikipage to an external page	http://videolectures.net/icml07_mimno_moht/
sameAs	Pachinko allocation Pachinko allocation Pachinko allocation Pachinko allocation Pachinko allocation Pachinko allocation
dbp:wikiPageUsesTemplate	dbt:Comp-sci-stub dbt:Expand_section dbt:Primary_sources dbt:Reflist dbt:Short_description dbt:Use_dmy_dates dbt:Natural_Language_Processing
has abstract	In machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model. Topic models are a suite of algorithms to uncover the hidden thematic structure of a collection of documents. The algorithm improves upon earlier topic models such as latent Dirichlet allocation (LDA) by modeling correlations between topics in addition to the word correlations which constitute topics. PAM provides more flexibility and greater expressive powerthan latent Dirichlet allocation. While first described and implemented in the context of natural language processing, the algorithm may have applications in other fields such as bioinformatics. Themodel is named for pachinko machines—a game popular in Japan, in which metal balls bounce down arounda complex collection of pins until they land in variousbins at the bottom. (en) Размещение патинко (англ. pachinko allocation, PAM) — метод тематического моделирования, применяемый в машинном обучении и обработке естественного языка, позволяющий обнаружить скрытую тематическую структуру в коллекции документов. От более ранних методов (например, LDA) алгоритм отличается тем, что моделирует корреляции между темами в дополнение к корреляциям слов, задающих темы. PAM превосходит LDA по гибкости и выразительной силе. Впервые метод описан, реализован и применён для обработки текстов на естественном языке, однако, может быть использован и в других областях, например, для задач биоинформатики. Своё название получил благодаря популярным в Японии игровым автоматам патинко, в которых реализована игра, напоминающая пинбол на доске Гальтона. (ru)
gold:hypernym	Model
prov:wasDerivedFrom	wikipedia-en:Pachinko_allocation?oldid=1103041695&ns=0
page length (characters) of wiki page	5380 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Pachinko_allocation
is Link from a Wikipage to another Wikipage of	Probabilistic latent semantic analysis Latent Dirichlet allocation Pachinko Outline of machine learning Topic model
is foaf:primaryTopic of	wikipedia-en:Pachinko_allocation

Faceted Search & Find service v1.17_git147 as of Sep 06 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3331 as of Sep 2 2024, on Linux (x86_64-generic-linux-glibc212), Single-Server Edition (378 GB total memory, 59 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software