Poker SOS

1. The screws I got must have been old pachinko machine screws - because they fit PERFECT and had the same 'cheap aged' look as the rest of the screws! If you take the machine apart have a clean open work.
2. Oct 5, 2018 - Came from Japan. The are in the arcades, like a pinball. Also I have been told they are used fr gambling also. They are loud and they are fun!! See more ideas about pinball.

• How Pachinko Works App
• How Pachinko Works Car
• How Pachinko Works Cited

Vintage Pachinko to Modern pachinko machines. 1980 is the year that most consider the cross over point from Vintage to modern pachinko, this is when most pachinkos went from a flipper.

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. ^[1] 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.^[2] 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.^[3]

History[edit]

Pachinko allocation was first described by Wei Li and Andrew McCallum in 2006.^[3]The idea was extended with hierarchical Pachinko allocation by Li, McCallum, and David Mimno in 2007.^[4] In 2007, McCallum and his colleagues proposed a nonparametric Bayesian prior for PAM basedon a variant of the hierarchical Dirichlet process (HDP).^[2] The algorithm has been implemented in the MALLET software package published by McCallum's group at the University of Massachusetts Amherst.

Model[edit]

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.

The probability of generating a whole corpus is the product of the probabilities for every document^[3]:

${\displaystyle P(\mathbf{D}\alpha )=\prod _{d}P(d\alpha )}$

How Pachinko Works App

See also[edit]

• Probabilistic latent semantic indexing (PLSI), an early topic model from Thomas Hofmann in 1999.^[5]
• Latent Dirichlet allocation, a generalization of PLSI developed by David Blei, Andrew Ng, and Michael Jordan in 2002, allowing documents to have a mixture of topics.^[6]
• MALLET, an open-source Java library that implements Pachinko allocation.

References[edit]

1. ^Blei, David. 'Topic modeling'. Archived from the original on 2 October 2012. Retrieved 4 October 2012.
2. ^ ^abLi, Wei; Blei, David; McCallum, Andrew (2007). 'Nonparametric Bayes Pachinko Allocation'. arXiv:1206.5270.Cite journal requires journal= (help)
3. ^ ^abcLi, Wei; McCallum, Andrew (2006). 'Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations'(PDF). Proceedings of the 23rd International Conference on Machine Learning.
4. ^Mimno, David; Li, Wei; McCallum, Andrew (2007). 'Mixtures of Hierarchical Topics with Pachinko Allocation'(PDF). Proceedings of the 24th International Conference on Machine Learning.
5. ^Hofmann, Thomas (1999). 'Probabilistic Latent Semantic Indexing'(PDF). Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval. Archived from the original(PDF) on 14 December 2010.
6. ^Blei, David M.; Ng, Andrew Y.; Jordan, Michael I; Lafferty, John (January 2003). 'Latent Dirichlet allocation'. Journal of Machine Learning Research. 3: pp. 993–1022. Archived from the original on 1 May 2012. Retrieved 19 July 2010.

How Pachinko Works Car

External links[edit]

How Pachinko Works Cited

• Mixtures of Hierarchical Topics with Pachinko Allocation, a video recording of David Mimno presenting HPAM in 2007.