Gerard Salton. Automatic Information Organization and Retrieval. McGraw-Hill, 1968. Home/Search Document Not in Database Summary Related Articles Check |
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Mathematical Models For Organizing Data Collections - Tanase (2002) (Correct)
....as a way for increasing the e#ciency of the search. Key words: similarity measure, dissimilarity measure, clustering, criterion function. 1. Introduction Mathematical organisation of data collections is based on three models: vector processing, logical and probabilistic. Vector processing model [4,5], materialised in the SMART system implementation has the best mathematical basis. In this model entities and queries have a vectorial representation and some similarities can be established between them based on the comparison of attached vectors. The similar entities will have answers for the ....
....into classes can be done using several determinist classifying methods. These methods are mainly optimisation procedures of some criterion functions. In order to build a criterion function we may consider that each class is represented by a geometrical prototype. In the vector processing model [4, 5], in which classes have an approximately spherical shape, the prototypes will be points in the Euclidean R space. 2. Similarity measures Let X be the space of entities to be classified. A similarity measure over X is a function S : X X # R, which satisfies the following axioms: a) S(x, y) # ....
Salton G., "Automatic Information Organization and Retrieval ", McGraw-Hill, New York, 1975
Info Navigator: A Visualization Tool for Document.. - Carey, Heesch, Rüger (2003) (Correct)
....over 10,000 different words. As an example, the four sets of top 500 documents returned by the queries mafia, Clinton, cancer and computer use a vocabulary of between 14,000 and 17,000 different candidate keywords. Document clustering has attracted much interest in the recent decades, eg [20, 8, 29, 17], and much is known about the importance of feature reduction in general, eg [14] and, in particular, clustering [27] However, little has been done so far to facilitate feature reduction for document clustering of query results, with the notable exception of [22] In contrast to the latter paper, ....
Gerard Salton. Automatic information organization and retrieval. McGraw-Hill, New York, 1968.
Logic and Uncertainty in Information Retrieval - Crestani, Lalmas (2001) (1 citation) (Correct)
....user s information need [83] Past and present research has made use of formal theories of probability and of statistics in order to evaluate, or at least estimate, those probabilities of relevance. These attempts are to be distinguished from looser ones like, for example, the vector space model [88] in which documents are ranked according to a measure of similarity with the query. A measure of similarity cannot be directly interpretable as a probability. In addition, similarity based models generally lack the theoretical soundness of probabilistic models. A treatment of models based on ....
G. Salton. Automatic information organization and retrieval. McGraw Hill, New York, 1968.
Using High Performance Systems to Build Collections for a.. - Bergmark (Correct)
....called focused crawling to find sets of URLs of documents related to specific subjects. To implement a focused crawl, we use both content analysis and link analysis. As HTML pages are downloaded, their words are extracted to build a weighted term vector which is then matched (cosine correlation [13]) against the term vectors (the centroids) representing each of our topic areas. The document is tentatively classed with the nearest subject vector, with the correlation (0.0 1.0) being the degree to which the document is considered to be in that collection. If the correlation is sufficiently ....
G. Salton. Automatic Information Organization and Retrieval. McGraw-Hill, New York, 1968.
Automatic Derivation of On-line Document Ontologies - Elliman, Pulido (2001) (Correct)
....person concerned with combustion processes, and to another interested in materials, as both factors may be discussed. However, the proximity of individual papers to one another, and to the researcher s key interests would ideally be quite di#erent. Our system can be outlined as a number of sets [14]: 1. Setofobjects(entities, concepts) 2. Set of functions (for example is a) 3. Setofrelations(between objects) 4. Set of semantic rules It can be inferred that an ontology should be produced in a bespoke manner to suit its purpose (Figure 4) This of course raises the crucial question of how ....
G Salton. Automatic Information Organization and Retrieval.McGraw- Hill, 1968.
Automatic Generation and Checking of Program Specifications - Nimmer (2001) (2 citations) (Correct)
....and ensures the absence of runtime exceptions. Measured against this verification requirement, the generated specifications scored nearly 90 on precision, a measure of soundness, and on recall, a measure of completeness. Precision and recall are standard measures from information retrieval [Sal68, vR79] Our results demonstrate that non trivial and useful aspects of program semantics are present in test executions, as measured by verifiability of generated specifications. Our results also demonstrate that the technique of dynamic invariant detection is effective in capturing this ....
....we counted the number of reported and verified invariants (the Verif column of Figure 4. 3) reported but unverifiable invariants (the Unver column) and unreported, but necessary, invariants (the Miss column) We computed precision and recall, standard measures from information retrieval [Sal68, vR79] based on these three numbers. Precision, a measure of soundness, is defined as Verif Verif Unver . Recall, a measure of completeness, is defined as Verif Verif Miss . For example, if Daikon reported 6 invariants (4 verifiable and 2 other unverifiable) while the verified set ....
Gerard Salton. Automatic Information Organization and Retrieval. McGraw-Hill, 1968.
Automatic Derivation of Document Ontologies - Pulido, al. (Correct)
.... studied these problems: how to extract knowledge from documents [Guarino, 1998] Borst et al. 1996] Gangemi et al. 1998] how to organize it [Salton et al. 1996] Sanz et al. 1998] Ackerman and Fielding, 1995] how to deliver it to users [Kohonen et al. 1999] Merkl, 1999] [Salton, 1968] however, most of the literature treats the problem in an isolated way Approach By using a combination of two relatively recent techniques: our method aims to extract knowledge from digital sources, and to create browsable and reusable collections of it This research may also provide an ....
.... a viewer, a search engine, or other software entities We aim to create a browsable collection of for a particular digital source Basic Approach analyze produce an agent community digital source OOO Object Oriented Ontology OOO This browsing tool can be outlined as a number of sets [Salton, 1968]: set of objects (entities, concepts) set of functions (for example: is a) set of relations (between objects) set of semantic rules no that rigid though Hierarchy Concept Entomologists classify insect using a system called taxonomy Take the case of Insects, they are ordered by families ....
Salton, G. (1968). Automatic Information Organization and Retrieval. McGraw-Hill.
Knowledge Discovery in Web-Directories: Finding - Term-Relations To Build (2005) (Correct)
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Gerard Salton. Automatic Information Organization and Retrieval. McGraw-Hill, 1968.
Relevance Feedback for Image Retrieval: a Short Survey - Crucianu, Ferecatu, Boujemaa (2004) (Correct)
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Gerard Salton. Automatic Information Organization and Retrieval. McGraw-Hill, 1968.
Unknown - (2004) (Correct)
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G. Salton, Automatic Information Organization and Retrieval, McGraw-Hill, 1968.
Formal Concept Analysis in Information Science (draft) - Priss (Correct)
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Salton, G. (1968). Automatic Information Organization and Retrieval. New York: McGraw-Hill.
Unknown - Andr Skupin Department (2005) (Correct)
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Salton, G. 1968. Automatic information organization and retrieval. New York: McGraw-Hill.
Approximative Filtering of XML Documents in a.. - Hinze, Michel, Schlieder (2006) (Correct)
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Salton, G. (1968), Automatic Information Organization and Retrieval, McGraw-Hill, New York.
Towards A Meaningful Inference Network - Model For Information (1994) (Correct)
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Salton, G., Automatic Information Organization and Retrieval, McGraw-Hill, 1968.
Selecting, Refining, and Evaluating Predicates for Program.. - Dodoo, Lin, Ernst (2003) (Correct)
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Gerard Salton. Automatic Information Organization and Retrieval. McGraw-Hill, 1968.
Automatic Generation of Program Specifications - Nimmer, Ernst (2002) (4 citations) (Correct)
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Gerard Salton. Automatic Information Organization and Retrieval. McGraw-Hill, 1968.
Dynamically Discovering Likely Program Invariants - Ernst (2000) (108 citations) (Correct)
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Gerard Salton. Automatic Information Organization and Retrieval. McGraw-Hill, 1968.
Finding Latent Code Errors via Machine Learning over Program.. - Brun, Ernst (2004) (4 citations) (Correct)
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G. Salton. Automatic Information Organization and Retrieval. McGraw-Hill, 1968.
Context-Sensitive Modeling of Web-Surfing Behaviour using.. - Acharyya, Ghosh (Correct)
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G. Salton. Automatic information organization and retrieval. McGraw Hill, New York, 1968.
Knowledge-based Access to Categorized Image Documents - Chabane Djeraba Irin (Correct)
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Salton Gerard "Automatic Information Organization and Retrieval", McGraw Hill Book Co, New York, 1968, Chapter 4.
Linguistic Variation in Information Retrieval Using Query.. - Kroon (2002) (Correct)
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Gerard Salton. Automatic Information Organization and Retrieval. McGraw-Hill, New York, NY, 1968.
Improving Test Suites via Operational Abstraction - Harder, Mellen, Ernst (2003) (14 citations) (Correct)
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Gerard Salton. Automatic Information Organization and Retrieval. McGraw-Hill, 1968.
Automatic Generation of Program Specifications - Nimmer, Ernst (2002) (4 citations) (Correct)
No context found.
Gerard Salton. Automatic Information Organization and Retrieval. McGraw-Hill, 1968.
Dynamically Discovering Likely Program Invariants - Ernst (2000) (108 citations) (Correct)
No context found.
Gerard Salton. Automatic Information Organization and Retrieval. McGraw-Hill, 1968.
Context-Sensitive Modeling of Web-Surfing Behaviour using.. - Acharyya, Ghosh (Correct)
No context found.
G. Salton. Automatic information organization and retrieval. McGraw Hill, New York, 1968.
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