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Showing 91 to 105 of 127 results Save | Export
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Egghe, L. – Information Processing and Management, 1988
Presents a mathematical theory that can be used to define concentration places of objects within unordered classes. The application to research on the evolution of journals and subject areas is illustrated, and an online method of calculating concentration evolution is described. (1 references) (CLB)
Descriptors: Algorithms, Bibliometrics, Classification, Databases
Peer reviewed Peer reviewed
Ribeiro-Neto, Berthier; Laender, Alberto H. F.; de Lima, Luciano R. S. – Journal of the American Society for Information Science and Technology, 2001
Evaluates the retrieval performance of an algorithm that automatically categorizes medical documents, which consists in assigning an International Code of Disease (ICD) based on well-known information retrieval techniques. Reports on experimental results that tested precision using a database of over 20,000 medical documents. (Author/LRW)
Descriptors: Algorithms, Automation, Classification, Databases
Cornell Univ., Ithaca, NY. Dept. of Computer Science. – 1970
Two papers are included as Part Four of this report on Salton's Magical Automatic Retriever of Texts (SMART) project report. The first paper: "A Controlled Single Pass Classification Algorithm with Application to Multilevel Clustering" by D. B. Johnson and J. M. Laferente presents a single pass clustering method which compares favorably…
Descriptors: Algorithms, Automation, Classification, Cluster Grouping
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Kar, Gautam; White, Lee J. – Information Processing and Management, 1978
Investigates the feasibility of using a distance measure for automatic sequential document classification. This property of the distance measure is used to design a sequential classification algorithm which classifies key words and analyzes them separately in order to assign primary and secondary classes to a document. (VT)
Descriptors: Algorithms, Automatic Indexing, Classification, Information Processing
Peer reviewed Peer reviewed
Rada, Roy – Information Processing and Management, 1987
Reviews aspects of the relationship between machine learning and information retrieval. Highlights include learning programs that extend from knowledge-sparse learning to knowledge-rich learning; the role of the thesaurus; knowledge bases; artificial intelligence; weighting documents; work frequency; and merging classification structures. (78…
Descriptors: Algorithms, Artificial Intelligence, Classification, Documentation
Longford, Nicholas T. – 1994
This study is a critical evaluation of the roles for coding and scoring of missing responses to multiple-choice items in educational tests. The focus is on tests in which the test-takers have little or no motivation; in such tests omitting and not reaching (as classified by the currently adopted operational rules) is quite frequent. Data from the…
Descriptors: Algorithms, Classification, Coding, Models
Peer reviewed Peer reviewed
Baker, Frank B. – Review of Educational Research, 1972
Reviews the potential and methodology of applying grouping algorithms and methodology of applying grouping algorithms to problems in educational research and practice. (JLB)
Descriptors: Algorithms, Classification, Educational Practices, Educational Research
Litofsky, Barry – 1969
Large-scale, on-line information storage and retrieval systems pose numerous problems above those encountered by smaller systems. A step toward the solution of these problems is presented along with several demonstrations of feasibility and advantages. The methodology on which this solution is based is that of a posteriori automatic classification…
Descriptors: Algorithms, Automation, Classification, Evaluation
Samad, Tariq – 1986
The application of the "back-propagation" learning algorithm to the task of determining the right set of features corresponding to the words in an input sentence is described. Features that are specific to particular nouns and verbs, that indicate whether a nominal constituent is singular or plural, definite or indefinite, and that…
Descriptors: Algorithms, Case (Grammar), Classification, Computer Storage Devices
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Zaki, M.; Elboraey, F. – Information Processing and Management, 1985
Investigates applicability of inverted file processing in magnetic bubble memories by presenting four magnetic bubble memory models as storage mediums: one based on a major-minor loop configuration; one utilizing a decoder design on a magnetic bubble memory chip; and two based on different structural configurations of the first two. (MBR)
Descriptors: Algorithms, Classification, Computer Storage Devices, Data Processing
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Gonzales, Michael G. – Computer Education, 1984
Suggests a moving pictorial tool to help teach principles in the bubble sort algorithm. Develops such a tool applied to an unsorted list of numbers and describes a method to derive the run time of the algorithm. The method can be modified to run the times of various other algorithms. (JN)
Descriptors: Algorithms, Classification, College Mathematics, Computer Programs
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Macready, George B.; Dayton, C. Mitchell – Psychometrika, 1992
An adaptive testing algorithm is presented based on an alternative modeling framework, and its effectiveness is investigated in a simulation based on real data. The algorithm uses a latent class modeling framework in which assessed latent attributes are assumed to be categorical variables. (SLD)
Descriptors: Adaptive Testing, Algorithms, Bayesian Statistics, Classification
Peer reviewed Peer reviewed
Price, Lydia J. – Multivariate Behavioral Research, 1993
The ability of the NORMIX algorithm to recover overlapping population structures was compared to the OVERCLUS procedure and another clustering procedure in a Monte Carlo study. NORMIX is found to be more accurate than other procedures in recovering overlapping population structure when appropriate implementation options are specified. (SLD)
Descriptors: Algorithms, Classification, Cluster Analysis, Comparative Analysis
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Sander, H. D.; Altmann, G. – Phonetica, 1973
Descriptors: Algorithms, Classification, Graphs, Linguistic Theory
Peer reviewed Peer reviewed
Schweizer, Karl – Multivariate Behavioral Research, 1991
A mathematical formula is introduced for the effect of integrating data. A method is then derived to eliminate the effect from correlations of variables, including mean composites, thus allowing for a clustering algorithm that requires allocation of variables according to the magnitude of their correlations. Examples illustrate the procedure. (SLD)
Descriptors: Algorithms, Classification, Cluster Analysis, Computer Simulation
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