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Losee, R.M.: Determining information retrieval and filtering performance without experimentation (1995) 0.04

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Abstract: The performance of an information retrieval or text and media filtering system may be determined through analytic methods as well as by traditional simulation or experimental methods. These analytic methods can provide precise statements about expected performance. They can thus determine which of 2 similarly performing systems is superior. For both a single query terms and for a multiple query term retrieval model, a model for comparing the performance of different probabilistic retrieval methods is developed. This method may be used in computing the average search length for a query, given only knowledge of database parameter values. Describes predictive models for inverse document frequency, binary independence, and relevance feedback based retrieval and filtering. Simulation illustrate how the single term model performs and sample performance predictions are given for single term and multiple term problems
Date: 22. 2.1996 13:14:10

Losee, R.M.: Term dependence : truncating the Bahadur Lazarsfeld expansion (1994) 0.01

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Source: Information processing and management. 30(1994) no.2, S.293-303

Losee, R.M.: When information retrieval measures agree about the relative quality of document rankings (2000) 0.01
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Abstract

The variety of performance measures available for information retrieval systems, search engines, and network filtering agents can be confusing to both practitioners and scholars. Most discussions about these measures address their theoretical foundations and the characteristics of a measure that make it desirable for a particular application. In this work, we consider how measures of performance at a point in a search may be formally compared. Criteria are developed that allow one to determine the percent of time or conditions under which 2 different performance measures suggest that one document ordering is superior to another ordering, or when the 2 measures disagree about the relative value of document orderings. As an example, graphs provide illustrations of the relationships between precision and F

Losee, R.M.: Seven fundamental questions for the science of library classification (1993) 0.01

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Source: Knowledge organization. 20(1993) no.2, S.65-70

Losee, R.M.: ¬The relative shelf location of circulated books : a study of classification, users, and browsing (1993) 0.01

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Source: Library resources and technical services. 37(1993) no.2, S.197-209

Losee, R.M.: Comparing Boolean and probabilistic information retrieval systems across queries and disciplines (1997) 0.01

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Source: Journal of the American Society for Information Science. 48(1997) no.2, S.143-156

Losee, R.M.: Upper bounds for retrieval performance and their user measuring performance and generating optimal queries : can it get any better than this? (1994) 0.01

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Source: Information processing and management. 30(1994) no.2, S.193-203

Losee, R.M.: Learning syntactic rules and tags with genetic algorithms for information retrieval and filtering : an empirical basis for grammatical rules (1996) 0.01

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Source: Information processing and management. 32(1996) no.2, S.185-197

Losee, R.M.: Browsing document collections : automatically organizing digital libraries and hypermedia using the Gray code (1997) 0.01

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Source: Information processing and management. 33(1997) no.2, S.175-192

Losee, R.M.: Browsing mixed structured and unstructured data (2006) 0.01

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Source: Information processing and management. 42(2006) no.2, S.440-452

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