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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. <ref>{{cite web|last=Blei|first=David|title=Topic modeling|url=http://www.cs.princeton.edu/~blei/topicmodeling.html|accessdate=4 October 2012}}</ref> 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 power
than latent Dirichlet allocation.<ref name="mccallum07">{{cite journal|last1=Li|first1=Wei|last2=Blei|first2=David|last3=McCallum|first3=Andrew|title=Nonparametric Bayes Pachinko Allocation|year=2007|url=http://www.cs.princeton.edu/~blei/papers/LiBleiMcCallum2007.pdf}}</ref>  While first described and implemented in the context of natural language processing, the algorithm may have applications in other fields such as [[bioinformatics]]. The
model is named for [[pachinko]] machines—a game popular in Japan, in which metal balls bounce down around
a complex collection of pins until they land in various
bins at the bottom.<ref name="li2006" />
 
==History==
Pachinko allocation was first described by Wei Li and [[Andrew McCallum]] in 2006.<ref name="li2006">{{cite journal
| last1 = Li | first1 = Wei
| last2 = McCallum |first2 = Andrew
| title = Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations
| journal = Proceedings of the 23rd [[International Conference on Machine Learning]]
| year = 2006
| url = http://www.cs.umass.edu/~mccallum/papers/pam-icml06.pdf
}}</ref>
The idea was extended with hierarchical Pachinko allocation by Li, McCallum, and David Mimno in 2007.<ref name="mimno2007">{{cite journal
| last1 = Mimno | first1 = David
| last2 = Li |first2 = Wei
| last3 = McCallum |first3 = Andrew
| title = Mixtures of Hierarchical Topics with Pachinko Allocation
| journal = Proceedings of the 24th [[International Conference on Machine Learning]]
| year = 2007
| url = http://www.machinelearning.org/proceedings/icml2007/papers/453.pdf
}}</ref> In 2007, McCallum and his colleagues proposed a nonparametric Bayesian prior for PAM based
on a variant of the hierarchical Dirichlet process (HDP).<ref name="mccallum07"/>  The algorithm has been implemented in the [[Mallet (software project)|MALLET]] software package published by McCallum's group at the [[University of Massachusetts Amherst]].
 
==Model==
 
 
 
PAM connects words in V and topics in T
with an arbitrary Directed Acyclic Graph (DAG), where topic nodes occupy the
interior levels and the leaves are words. Two possible
model structures are shown on the right labeled c and d. <ref name="li2006" />
 
The probability of generating a whole corpus
is the product of the probability for every document:
 
<math>P(\boldsymbol{D}|\alpha) = \prod_d P(d|\alpha)</math>
 
==See also==
* [[Probabilistic latent semantic indexing]] (PLSI), an early topic model from Thomas Hofmann in 1999.<ref name="hofmann1999">{{cite journal
| last1 = Hofmann | first1 = Thomas
| title = Probabilistic Latent Semantic Indexing
| journal = Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval
| year = 1999
| url = http://www.cs.brown.edu/~th/papers/Hofmann-SIGIR99.pdf
}}</ref>
* [[Latent Dirichlet allocation]], a generalization of PLSI developed by [[David Blei]], [[Andrew Ng]], and [[Michael I. Jordan|Michael Jordan]] in 2002, allowing documents to have a mixture of topics.<ref name="blei2003">{{cite journal
| last1 = Blei | first1 = David M.
| last2 = Ng |first2 = Andrew Y.
| last3 = Jordan |first3 = Michael I |authorlink3=Michael I. Jordan
| title = Latent Dirichlet allocation
| journal = [[Journal of Machine Learning Research]]
|date=January 2003
| volume = 3
| pages = ''pp.'' 993&ndash;1022
| url = http://jmlr.csail.mit.edu/papers/v3/blei03a.html
| doi = 10.1162/jmlr.2003.3.4-5.993
| last4 = Lafferty
| first4 = John
}}</ref>
* [[Mallet (software project)|MALLET]], an open-source Java library that implements Pachinko allocation.
 
==References==
{{Reflist}}
 
==External links==
* [http://videolectures.net/icml07_mimno_moht/ Mixtures of Hierarchical Topics with Pachinko Allocation], a video recording of David Mimno presenting HPAM in 2007.
 
{{DEFAULTSORT:Pachinko Allocation}}
[[Category:Statistical natural language processing]]
[[Category:Latent variable models]]
 
 
{{comp-sci-stub}}

Latest revision as of 00:25, 9 November 2014

Ceramic blade knives seem to be something that professional cooks handle with loving care. Composed of zirconium oxide, they're accessible in white or black blades which have been subjected to higher strain and firing to release zirconium carbide. Whereas chefs could like the ceramic blade, so do many pocket knife lovers. Ceramic blade pocket knives tend to be becoming increasingly aero pocket knife more accepted. They are typically an awesome deal costlier than cast metallic knives and several tend to be put together overseas, and so the government adds further import charges on prime of the more expensive value of supplies.

Within the knife group Chris Reeve is synonymous with high quality and innovation and no dialogue of greatest pocket knives is full with out mentioning the Sebenza. The Chris Reeve Sebenza has long been regarded by the trade as probably the greatest folding knives cash should purchase. It has a titanium body lock design that mixes simplicity with sturdiness and a blade made from S35VN stainless-steel that without end retains its edge. As soon as opened and locked the knife feels as solid as a fixed blade and holds comfortably in the hand. The quality is actually second to none and it’ll shave the hairs on your chin proper out of the field!

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The majority of the knife world is just about normally consensus that a non-serrated blade is greatest for on a regular basis carry. Some may disagree, as their on a regular basis tasks could embody reducing rope and such. As we famous above, if you’re utilizing a knife in these conditions you must seemingly upgrade to a work knife with a serrated edge, not an EDC pocket knife. A easy sharp blade should be all you need for an EDC pocket knife, permitting you to make clean exact cuts. You’re not Crocodile Dundee, so that you don’t need to EDC a fixed blade. Folding knives are far more compact and simpler to hold on your particular person.