Inflationism - Revision history

en>ClueBot NG: Reverting possible vandalism by Whats that mean to version by 208.50.124.65. False positive? Report it. Thanks, ClueBot NG. (1986237) (Bot)

2014-10-10T01:04:26Z

Reverting possible vandalism by Whats that mean to version by 208.50.124.65. False positive? Report it. Thanks, ClueBot NG. (1986237) (Bot)

← Older revision		Revision as of 03:04, 10 October 2014
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	'''Uncertain inference''' was first described by Rijsbergen<ref>{{cite \| author=C. J. van Rijsbergen \| title=A non-classical logic for information retrieval \| publisher=The Computer Journal \| pages=481–485 \| year=1986}}</ref> as a way to formally define a query and document relationship in [[Information retrieval]]. This formalization is a [[logical consequence\|logical implication]] with an attached measure of uncertainty.		I am Whitney from Stykkisholmur studying Hotel Administration. I did my schooling, secured 88% and hope to find someone with same interests in Sculling or Rowing.<br><br>Feel free to surf to my weblog [http://www.olwallpaper.com/profile/vigula Biking for modern life mountain bike sizing.]

	~~==Definitions==~~
	~~Rijsbergen proposes that the measure of uncertainty of a document ''d'' to a query ''q'' be the probability of its logical implication, i.e~~.:

	~~<math>P(d \to q)</math>~~

	~~A user's query can be interpreted as a set of assertions about the desired document. It is the system's task to infer~~, ~~given a particular document, if the query assertions are true. If they are, the document is retrieved.~~
	~~In many cases the contents of documents are not sufficient to assert the queries. A knowledge base of facts~~ and ~~rules is needed, but some of them may be uncertain because there may be a probability associated~~ to ~~using them for inference. Therefore, we can also refer to this as ''plausible inference''~~. ~~The plausibility of an inference~~ <~~math~~>~~d \to q~~<~~/math~~> ~~is a function of the plausibility of each query assertion. Rather than retrieving a document that exactly matches the query we should rank the documents based on their plausibility in regards~~ to ~~that query.~~
	~~Since ''d'' and ''q'' are both generated by users, they are error prone; thus <math>d \to q</math> is uncertain. This will affect the plausibility of a given query.~~

	~~By doing this it accomplishes two things:~~
	* Separate the processes of revising probabilities from the logic
	* Separate the treatment of relevance from the treatment of requests

	Multimedia documents, like images or videos, have different inference properties for each datatype. They are also different from text document properties. The framework of plausible inference allows us to ~~measure and combine the probabilities coming from these different properties.~~

	~~Uncertain inference generalizes the notions of~~ [~~[autoepistemic logic]], where truth values are either known or unknown, and when known, they are true or false.~~

	~~==Example==~~
	~~If we have a query of the form:~~

	~~<math>q = A \wedge B \wedge C</math>~~

	~~where A, B and C are query assertions, then for a document D we want the probability:~~

	~~<math>P (D \to (A \wedge B \wedge C))</math>~~

	If we transform this into the conditional probability <math>P ((A \wedge B \wedge C) \| D)</math> and if the query assertions are independent we can calculate the overall probability of the implication as the product of the individual assertions probabilities.

	~~==Further work==~~
	~~Croft and Krovetz<ref>{{cite \| title=Interactive retrieval office documents \| url=~~http://~~doi~~.~~acm~~.~~org~~/~~10.1145~~/45410.45435 \| author=W. B. Croft \| coauthors=R. Krovetz \| year=1988 }}</ref> applied uncertain inference to an information retrieval system for office documents they called ''OFFICER''. In office documents the independence assumption is valid since the query will focus on their individual attributes. Besides analysing the content of documents one can also query about the author, size, topic or collection for ~~example~~. They devised methods to compare document and query attributes, infer their plausibility and combine it into an overall rating for each document. Besides that uncertainty of document and query contents also had to be addressed.

	[[Probabilistic logic network]]s is a system for performing uncertain inference; crisp true/false truth values are replaced not only by a probability, but also by a confidence level, indicating the certitude of the probability.

	[[Markov logic network]]s allow uncertain inference to be performed; uncertainties are computed using the [[maximum entropy principle]], in analogy to the way that [[Markov chain]]s describe the uncertainty of [[finite state machine]]s.

	~~== See also ==~~
	* [[Fuzzy logic]]
	* [[Probabilistic logic]]
	* [[Imprecise probability]]

	~~==References==~~
	~~{{reflist}}~~

	~~[[Category:Fuzzy logic]]~~
	~~[[Category:Information retrieval]]~~
	~~[[Category:Inference]~~]

en>Byelf2007: alphabetical

2012-10-01T19:17:55Z

alphabetical

← Older revision		Revision as of 21:17, 1 October 2012
Line 1:		Line 1:
			'''Uncertain inference''' was first described by Rijsbergen<ref>{{cite \| author=C. J. van Rijsbergen \| title=A non-classical logic for information retrieval \| publisher=The Computer Journal \| pages=481–485 \| year=1986}}</ref> as a way to formally define a query and document relationship in [[Information retrieval]]. This formalization is a [[logical consequence\|logical implication]] with an attached measure of uncertainty.

			==Definitions==
			Rijsbergen proposes that the measure of uncertainty of a document ''d'' to a query ''q'' be the probability of its logical implication, i.e.:

	~~The feeling~~ of ~~freedom and self-control that you feel as you drive through~~ the ~~mountain trails can help you forget all~~ the ~~stress and worries of daily life~~. ~~Therefore~~, ~~you ought to verify~~ the ~~fork,~~ the ~~wheels and~~ the ~~suspensions together with two or three particulars~~. ~~While a well trained dolphin~~ is ~~not as intimidating as~~ a ~~close encounter with a 230 kg wild animal is a totally different experience~~. ~~There should not be a strain and similarly you shouldn~~'~~t find yourself catching your breath. That attitude completely misses what is important about Papago Park~~. <br><br>~~Once you are sure~~ that ~~this is what you want~~ to ~~do, it is now time for you to decide on the bike~~. ~~If you cherished this article~~ and ~~also you would like~~ to ~~acquire more info relating to [http:~~/~~/vcsc~~.~~cs.uh.edu/second-computing/view_profile.php?userid=5781621 Women mountain bike sizing.] i implore you to visit~~ the ~~web-site. General Use (Commuting, Mountain Bike, Road Bike, BMX, and Universal Fit) General use helmets are usually about~~. ~~So how exactly would you go about making~~ this ~~dream a reality. You can travel on~~ the ~~equivalent~~ of ~~10 cents a gallon~~ of ~~gas. It is a choice you make that determines~~ the ~~level~~ of ~~commitment you~~ have. ~~<br><br>Folding bike tips on~~ different ~~types~~ of ~~foldable bikes. People now prefer~~ to ~~have~~ the ~~simplest design in their bikes as long as it can get~~ the ~~job well done. Buy a bike that has a top quality body made outside~~ of ~~steel~~, ~~aluminum~~, ~~carbon fiber~~ or ~~titanium~~. ~~It is pretty incredible how much of~~ a ~~significant difference Haro mountain bike can have,~~ of ~~course you have to think about certain factors. You might want to pitch a tent or you may find that there are many other options for lodging as you move along~~ the ~~trails on your mountain biking trips.~~ <br><br>~~The tracks are mostly dirt paths on outdoor scenery. In between~~, ~~the biker can perform whatever new trick he or she has learnt up~~ and ~~this trick is known as the gap jump. But older children may have more adventurous tastes~~, ~~and~~ want ~~for toys that are - well, still miniatures, but are flashier, more expensive, and more functional. People like to ride~~ the ~~mountain bike~~ to ~~those terrains because they enjoy~~ the ~~thrill~~ and the ~~adventure. You should be selective in choosing mountain bike tires since mountain bike is already made heavy and if you choose heavy tires you should consider yourself about how you will handle~~ the ~~bike and~~ the ~~terrain at~~ the ~~same time~~. <br><br>~~If you can afford it, a full suspension mountain bike~~ is ~~always worth the purchase. Listed below are some of~~ the ~~common terms which you~~ will ~~come across~~. ~~The price may generally differ depending on~~ the ~~condition~~ of the ~~bike~~ and ~~on how new the model really is~~. ~~The price range~~ of ~~lightweight bicycles prices vary from $200~~. ~~It'~~s ~~about the journey and all that it entails~~, ~~learning about yourself~~, ~~making friends, being helped, and~~ of ~~course~~, ~~helping others along~~ the way".		<math>P(d \to q)</math>

			A user's query can be interpreted as a set of assertions about the desired document. It is the system's task to infer, given a particular document, if the query assertions are true. If they are, the document is retrieved.
			In many cases the contents of documents are not sufficient to assert the queries. A knowledge base of facts and rules is needed, but some of them may be uncertain because there may be a probability associated to using them for inference. Therefore, we can also refer to this as ''plausible inference''. The plausibility of an inference <math>d \to q</math> is a function of the plausibility of each query assertion. Rather than retrieving a document that exactly matches the query we should rank the documents based on their plausibility in regards to that query.
			Since ''d'' and ''q'' are both generated by users, they are error prone; thus <math>d \to q</math> is uncertain. This will affect the plausibility of a given query.

			By doing this it accomplishes two things:
			* Separate the processes of revising probabilities from the logic
			* Separate the treatment of relevance from the treatment of requests

			Multimedia documents, like images or videos, have different inference properties for each datatype. They are also different from text document properties. The framework of plausible inference allows us to measure and combine the probabilities coming from these different properties.

			Uncertain inference generalizes the notions of [[autoepistemic logic]], where truth values are either known or unknown, and when known, they are true or false.

			==Example==
			If we have a query of the form:

			<math>q = A \wedge B \wedge C</math>

			where A, B and C are query assertions, then for a document D we want the probability:

			<math>P (D \to (A \wedge B \wedge C))</math>

			If we transform this into the conditional probability <math>P ((A \wedge B \wedge C) \| D)</math> and if the query assertions are independent we can calculate the overall probability of the implication as the product of the individual assertions probabilities.

			==Further work==
			Croft and Krovetz<ref>{{cite \| title=Interactive retrieval office documents \| url=http://doi.acm.org/10.1145/45410.45435 \| author=W. B. Croft \| coauthors=R. Krovetz \| year=1988 }}</ref> applied uncertain inference to an information retrieval system for office documents they called ''OFFICER''. In office documents the independence assumption is valid since the query will focus on their individual attributes. Besides analysing the content of documents one can also query about the author, size, topic or collection for example. They devised methods to compare document and query attributes, infer their plausibility and combine it into an overall rating for each document. Besides that uncertainty of document and query contents also had to be addressed.

			[[Probabilistic logic network]]s is a system for performing uncertain inference; crisp true/false truth values are replaced not only by a probability, but also by a confidence level, indicating the certitude of the probability.

			[[Markov logic network]]s allow uncertain inference to be performed; uncertainties are computed using the [[maximum entropy principle]], in analogy to the way that [[Markov chain]]s describe the uncertainty of [[finite state machine]]s.

			== See also ==
			* [[Fuzzy logic]]
			* [[Probabilistic logic]]
			* [[Imprecise probability]]

			==References==
			{{reflist}}

			[[Category:Fuzzy logic]]
			[[Category:Information retrieval]]
			[[Category:Inference]]

en>Nbarth: /* Contemporary advocacy */ typo

2012-06-01T10:22:17Z

Contemporary advocacy: typo

New page

The feeling of freedom and self-control that you feel as you drive through the mountain trails can help you forget all the stress and worries of daily life. Therefore, you ought to verify the fork, the wheels and the suspensions together with two or three particulars. While a well trained dolphin is not as intimidating as a close encounter with a 230 kg wild animal is a totally different experience. There should not be a strain and similarly you shouldn't find yourself catching your breath. That attitude completely misses what is important about Papago Park. Once you are sure that this is what you want to do, it is now time for you to decide on the bike. If you cherished this article and also you would like to acquire more info relating to [http://vcsc.cs.uh.edu/second-computing/view_profile.php?userid=5781621 Women mountain bike sizing.] i implore you to visit the web-site. General Use (Commuting, Mountain Bike, Road Bike, BMX, and Universal Fit) General use helmets are usually about. So how exactly would you go about making this dream a reality. You can travel on the equivalent of 10 cents a gallon of gas. It is a choice you make that determines the level of commitment you have. Folding bike tips on different types of foldable bikes. People now prefer to have the simplest design in their bikes as long as it can get the job well done. Buy a bike that has a top quality body made outside of steel, aluminum, carbon fiber or titanium. It is pretty incredible how much of a significant difference Haro mountain bike can have, of course you have to think about certain factors. You might want to pitch a tent or you may find that there are many other options for lodging as you move along the trails on your mountain biking trips. The tracks are mostly dirt paths on outdoor scenery. In between, the biker can perform whatever new trick he or she has learnt up and this trick is known as the gap jump. But older children may have more adventurous tastes, and want for toys that are - well, still miniatures, but are flashier, more expensive, and more functional. People like to ride the mountain bike to those terrains because they enjoy the thrill and the adventure. You should be selective in choosing mountain bike tires since mountain bike is already made heavy and if you choose heavy tires you should consider yourself about how you will handle the bike and the terrain at the same time. If you can afford it, a full suspension mountain bike is always worth the purchase. Listed below are some of the common terms which you will come across. The price may generally differ depending on the condition of the bike and on how new the model really is. The price range of lightweight bicycles prices vary from $200. It's about the journey and all that it entails, learning about yourself, making friends, being helped, and of course, helping others along the way".