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Fuzzy Retrieval
Fuzzy retrieval techniques are based on the Extended Boolean model and the Fuzzy set theory. There are two classical fuzzy retrieval models: Mixed Min and Max (MMM) and the Paice model. Both models do not provide a way of evaluating query weights, however this is considered by the P-norms algorithm. Mixed Min and Max model (MMM) In fuzzy-set theory, an element has a varying degree of membership, say ''dA'', to a given set ''A'' instead of the traditional membership choice (is an element/is not an element). In MMM each index term has a fuzzy set associated with it. A document's weight with respect to an index term ''A'' is considered to be the degree of membership of the document in the fuzzy set associated with ''A''. The degree of membership for union and intersection are defined as follows in Fuzzy set theory: :d_= min(d_A, d_B) :d_= max(d_A,d_B) According to this, documents that should be retrieved for a query of the form ''A or B'', should be in the fuzzy set associated with ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] [Amazon] |
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Extended Boolean Model
The Extended Boolean model was described in a Communications of the ACM article appearing in 1983, by Gerard Salton, Edward A. Fox, and Harry Wu. The goal of the Extended Boolean model is to overcome the drawbacks of the Boolean model that has been used in information retrieval. The Boolean model doesn't consider term weights in queries, and the result set of a Boolean query is often either too small or too big. The idea of the extended model is to make use of partial matching and term weights as in the vector space model. It combines the characteristics of the Vector Space Model with the properties of Boolean algebra and ranks the similarity between queries and documents. This way a document may be somewhat relevant if it matches some of the queried terms and will be returned as a result, whereas in the Standard Boolean model it wasn't. Thus, the extended Boolean model can be considered as a generalization of both the Boolean and vector space models; those two are special cases if ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] [Amazon] |
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Fuzzy Set
Fuzzy or Fuzzies may refer to: Music * Fuzzy (band), a 1990s Boston indie pop band * Fuzzy (composer), Danish composer Jens Vilhelm Pedersen (born 1939) * Fuzzy (album), ''Fuzzy'' (album), 1993 debut album of American rock band Grant Lee Buffalo * "Fuzzy", a song from the 2009 ''Collective Soul (2009 album), Collective Soul'' album by Collective Soul * "Fuzzy", a song from ''Poppy.Computer'', the debut 2017 album by Poppy * Fuzzy, an Australian events company that organises Listen Out, a multi-city Australian music festival Nickname * Faustina Agolley (born 1984), Australian television presenter, host of the Australian television show ''Video Hits'' * Fuzzy Haskins (1941–2023), American singer and guitarist with the doo-wop group Parliament-Funkadelic * Fuzzy Hufft (1901−1973), American baseball player * Fuzzy Knight (1901−1976), American actor * Andrew Levane (1920−2012), American National Basketball Association player and coach * Robert Alfred Theobald (1884−1957), Uni ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] [Amazon] |
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Standard Boolean Model
The (standard) Boolean model of information retrieval (BIR) is a classical information retrieval (IR) model and, at the same time, the first and most-adopted one. The BIR is based on Boolean logic and classical set theory in that both the documents to be searched and the user's query are conceived as sets of terms (a bag-of-words model). Retrieval is based on whether or not the documents contain the query terms and whether they satisfy the boolean conditions described by the query. Definitions An ''index term'' is a word or expression'','' which may be stemmed, describing or characterizing a document, such as a keyword given for a journal article. LetT = \be the set of all such index terms. A ''document'' is any subset of T. LetD = \be the set of all documents. T is a series of words or small phrases (index terms). Each of those words or small phrases are named t_n, where n is the number of the term in the series/list. You can think of T as "Terms" and t_n as "index term ' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] [Amazon] |
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Christopher D
Christopher is the English version of a Europe-wide name derived from the Greek name Χριστόφορος (''Christophoros'' or '' Christoforos''). The constituent parts are Χριστός (''Christós''), "Christ" or "Anointed", and φέρειν (''phérein''), "to bear"; hence the "Christ-bearer". As a given name, 'Christopher' has been in use since the 10th century. In English, Christopher may be abbreviated as "Chris", "Topher", and sometimes " Kit". It was frequently the most popular male first name in the United Kingdom, having been in the top twenty in England and Wales from the 1940s until 1995, although it has since dropped out of the top 100. Within the United Kingdom, the name is most common in England and not so common in Wales, Scotland, or Northern Ireland. Cognates in other languages *Afrikaans: Christoffel, Christoforus * Albanian: Kristofer, Kristofor, Kristoforid, Kristo *Arabic: كريستوفر (''Krīstafor, Kristūfar, Krístufer''), اصطفر (''ʔi� ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] [Amazon] |
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Precision And Recall
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. Written as a formula: \text = \frac Recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Written as a formula: \text = \frac Both precision and recall are therefore based on relevance. Consider a computer program for recognizing dogs (the relevant element) in a digital photograph. Upon processing a picture which contains ten cats and twelve dogs, the program identifies eight dogs. Of the eight elements identified as dogs, only five actually are dogs ( true positives), while the other three are cats ( false positives). Seven dogs were missed ( false negatives), and seven cats were correctly ex ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] [Amazon] |
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Information Retrieval
Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an Information needs, information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text search, full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds. Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; it also stores and manages those documents. Web search engines are the most visible IR applications. Overview An information retrieval process begins when a user enters a query into the sys ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] [Amazon] |