en>Mogism: /* Ranking the Elements of Descriptor */Typo fixing and cleanup, typos fixed: of of → of using AWB (8414)

2012-10-29T19:28:10Z

Ranking the Elements of Descriptor: Typo fixing and cleanup, typos fixed: of of → of using AWB (8414)

← Older revision		Revision as of 21:28, 29 October 2012
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	~~Quit Smoking Hypnosis CD - Zero Cravings, Zero Weight Gain!~~<br><br>		{{context\|date=March 2012}}
			'''Dynamic topic models''' are [[generative model]]s that can be used to analyze the evolution of (unobserved) topics of a collection of documents over time. This family of models was proposed by [[David Blei]] and John Lafferty and is an extension to [[Latent Dirichlet Allocation]] (LDA) that can handle sequential documents.<ref name='dtm'>{{cite journal\|last=Blei\|first=David M\|coauthors=Lafferty, John D\|title=Dynamic topic models\|journal=Proceedings of the ICML\|year=2006\|pages=113–120\|series=ICML'06\|isbn=1-59593-383-2}}</ref>

	~~The best way~~ to ~~stop smoking is actually~~ the ~~way~~ in ~~which works toward your strengths~~. ~~You have probably tried things previously~~ and ~~succeeded~~ and ~~failed at them~~, ~~possibly even with stopping smoking~~. So the ~~first thing to do~~ is ~~usually~~ to ~~appreciate everyday these past successes~~ and ~~failures~~ and ~~pick~~ the ~~right way to stop smoking~~.<br><br>~~It is also trouble~~ for ~~those around us who may have had their head bitten off a moment or two! Being crabby with other people has been enough of an excuse will be able to buy another pack of cigs before~~, ~~but once I accepted which it was justification to satisfy~~ the ~~cravings~~ and ~~resisted temptation I knew that I was on my way to~~ as ~~a non smoker once~~ and ~~for all.~~<br><br>~~Over~~ the ~~years there~~ has ~~been efforts by health care professionals~~ to ~~implement programs~~ to ~~aid visitors~~ to ~~quit smoking~~. ~~There are also a great deal~~ of ~~products to assist~~ people ~~who have~~ the ~~addiction~~. ~~One alternative method that has been utilized more than 3~~,~~000 years~~ is ~~acupuncture~~ and ~~Chinese medicine~~.<br><br>1. ~~Avoid in the crowd of smokers. I used to mingle with individuals who smoke within my~~ time ~~as a smoker~~, ~~This happens on one occasion when~~ I ~~suffered a smokers cough. This lasted in excess of one month.~~ I ~~could hardly sleep all night long as~~ a ~~result of my severe cough. Any medicine~~ I ~~took~~, ~~it turned out useless~~, it'~~s got no effect whatsoever~~.<br><br>It is ~~important~~ that ~~you proceed with care since wrong~~ use of ~~nicotine relief products could actually result~~ in the ~~dependence on worsen. Nicotine gums~~ and ~~patches are offered in a very range of dosage levels that is comparative for~~ the ~~amount you smoke in a very day~~. In ~~order~~ to ~~properly start using these products you must commence with~~ the ~~dosage level signifying your overall smoking amount and after~~ that ~~slowly limit the dosage until it~~ can be ~~gone completely~~. ~~However,~~ the ~~use~~ of ~~way too high a dosage you are able~~ to ~~cause~~ the ~~addiction to worsen, putting yourself back a number~~ of ~~steps on the road to being smoke free~~.<br><br>~~If you loved this short article and you want to receive much more information concerning scissor cutter~~ [~~just click the up coming web site~~] ~~assure visit our webpage.~~		In LDA, both the order the words appear in a document and the order the documents appear in the corpus are oblivious to the model. Whereas words are still assumed to be [[De Finetti's theorem\|exchangeable]], in a dynamic topic model the order of the documents plays a fundamental role. More precisely, the documents are grouped by time slice (e.g.: years) and it is assumed that the documents of each group come from a set of topics that evolved from the set of the previous slice.

			==Topics==
			Similarly to [[Latent Dirichlet allocation\|LDA]] and [[pLSA]], in a dynamic topic model, each document is viewed as a mixture of unobserved topics. Furthermore, each topic defines a [[multinomial distribution]] over a set of terms. Thus, for each word of each document, a topic is drawn from the mixture and a term is subsequently drawn from the multinomial distribution corresponding to that topic.

			The topics, however, evolve over time. For instance, the two most likely terms of a topic at time {{math\|t}} could be "network" and "Zipf" (in descending order) while the most likely ones at time {{math\|t+1}} could be "Zipf" and "percolation" (in descending order).

			==Model==

			Define
			: <math>\alpha_t</math> as the per-document topic distribution at time ''t''.
			: <math>\beta_{t,k}</math> as the word distribution of topic ''k'' at time ''t''.
			: <math>\theta_{t,d}</math> as the topic distribution for document ''d'' in time ''t'',
			: <math>z_{t,d,n}</math> as the topic for the ''n''th word in document ''d'' in time ''t'', and
			: <math>w_{t,d,n}</math> as the specific word.

			In this model, the multinomial distributions <math>\alpha_{t+1}</math> and <math>\beta_{t+1,k}</math> are generated from <math>\alpha_t</math> and <math>\beta_{t,k}</math>, respectively.
			Even though multinomial distributions are usually written in terms of the mean parameters, representing them in terms of the natural parameters is better in the context of dynamic topic models.

			The former representation has some disadvantages due to the fact that the parameters are constrained to be non-negative and sum to one.<ref>{{cite web\|last=Rennie\|first=Jason D. M.\|title=Mixtures of Multinomials\|url=http://people.csail.mit.edu/~jrennie/writing/mixtureMultinomials.pdf\|accessdate=5 December 2011}}</ref> When defining the evolution of these distributions, one would need to assure that such constraints were satisfied. Since both distributions are in the [[exponential family]], one solution to this problem is to represent them in terms of the natural parameters, that can assume any real value and can be individually changed.

			Using the natural parameterization, the dynamics of the topic model are given by
			:<math>\beta_{t,k}\|\beta_{t-1,k} \sim N(\beta_{t-1,k},\sigma^2 I)</math>
			and
			:<math>\alpha_{t}\|\alpha_{t-1} \sim N(\alpha_{t-1},\delta^2 I)</math>.

			The generative process at time slice 't' is therefore:
			# Draw topics <math>\beta_{t,k}\|\beta_{t-1,k} \sim N(\beta_{t-1,k},\sigma^2 I) \forall k</math>
			# Draw mixture model <math>\alpha_{t}\|\alpha_{t-1} \sim N(\alpha_{t-1},\delta^2 I)</math>
			# For each document:
			## Draw <math>\eta_{t,d} \sim N(\alpha_t,a^2 I)</math>
			## For each word:
			### Draw topic <math>Z_{t,d,n} \sim \textrm{Mult}(\pi(\eta_{t,d}))</math>
			### Draw word <math>W_{t,d,n} \sim \textrm{Mult}(\pi(\beta_{t,Z_{t,d,n}}))</math>

			where <math>\pi(x)</math> is a mapping from the natural parameterization ''x'' to the mean parameterization, namely
			:<math>\pi(x_i) = \frac{\exp(x_i)}{\sum_i \exp(x_i)}</math>.

			==Inference==

			In the dynamic topic model, only <math>W_{t,d,n}</math> is observable. Learning the other parameters constitutes an inference problem. Blei and Lafferty argue that applying [[Gibbs sampling]] to do inference in this model is more difficult than in static models, due to the nonconjugacy of the Gaussian and multinomial distributions. They propose the use of [[variational methods]], in particular, the Variational Kalman Filtering and the Variational Wavelet Regression.

			==Application==

			In the original paper, a dynamic topic model is applied to the corpus of Science articles published between 1881 to 1999 aiming to show that this method can be used to analyze the trends of word usage inside topics.<ref name="dtm" /> The authors also show that the model trained with past documents is able to fit documents of an incoming year better than LDA.

			A continuous dynamic topic model was developed by Wang et al. and applied to predict the timestamp of documents.<ref>{{cite journal\|last=Wang\|first=Chong\|coauthors=Blei, David and Heckerman, David\|title=Continuous Time Dynamic Topic Models\|journal=Proceedings of ICML\|year=2008\|series=ICML '08}}</ref>

			== References ==
			{{reflist}}
			*
			*
			*
			*

			[[Category:Latent variable models]]
			[[Category:Statistical natural language processing]]

en>Helpful Pixie Bot: Fixed header Reference => References (Build J2)

2012-03-12T06:51:02Z

Fixed header Reference => References (Build J2)

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Quit Smoking Hypnosis CD - Zero Cravings, Zero Weight Gain! 

The best way to stop smoking is actually the way in which works toward your strengths. You have probably tried things previously and succeeded and failed at them, possibly even with stopping smoking. So the first thing to do is usually to appreciate everyday these past successes and failures and pick the right way to stop smoking. It is also trouble for those around us who may have had their head bitten off a moment or two! Being crabby with other people has been enough of an excuse will be able to buy another pack of cigs before, but once I accepted which it was justification to satisfy the cravings and resisted temptation I knew that I was on my way to as a non smoker once and for all. Over the years there has been efforts by health care professionals to implement programs to aid visitors to quit smoking. There are also a great deal of products to assist people who have the addiction. One alternative method that has been utilized more than 3,000 years is acupuncture and Chinese medicine. 1. Avoid in the crowd of smokers. I used to mingle with individuals who smoke within my time as a smoker, This happens on one occasion when I suffered a smokers cough. This lasted in excess of one month. I could hardly sleep all night long as a result of my severe cough. Any medicine I took, it turned out useless, it's got no effect whatsoever. It is important that you proceed with care since wrong use of nicotine relief products could actually result in the dependence on worsen. Nicotine gums and patches are offered in a very range of dosage levels that is comparative for the amount you smoke in a very day. In order to properly start using these products you must commence with the dosage level signifying your overall smoking amount and after that slowly limit the dosage until it can be gone completely. However, the use of way too high a dosage you are able to cause the addiction to worsen, putting yourself back a number of steps on the road to being smoke free. If you loved this short article and you want to receive much more information concerning scissor cutter [just click the up coming web site] assure visit our webpage.

Rank SIFT - Revision history

en>Mogism: /* Ranking the Elements of Descriptor */Typo fixing and cleanup, typos fixed: of of → of using AWB (8414)

en>Helpful Pixie Bot: Fixed header Reference => References (Build J2)