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| Information Retrieval | 2 |
| Learning Processes | 2 |
| Models | 2 |
| Evaluation Methods | 1 |
| Feedback | 1 |
| Indexing | 1 |
| Induction | 1 |
| Information Networks | 1 |
| Information Processing | 1 |
| Mathematical Models | 1 |
| Relevance (Information… | 1 |
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| Information Processing &… | 2 |
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| Journal Articles | 2 |
| Reports - Descriptive | 1 |
| Reports - Evaluative | 1 |
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Peer reviewedAmati, Gianni; Crestani, Fabio – Information Processing & Management, 1999
Describes and evaluates a learning model for information filtering and selective dissemination of information which is an adaptation of the generalized probabilistic model of information retrieval. The model is based on the concept of uncertainty sampling that allows for relevance feedback both on relevant and nonrelevant documents. (Author/LRW)
Descriptors: Evaluation Methods, Feedback, Information Retrieval, Learning Processes
Peer reviewedCortez, Edwin M.; And Others – Information Processing & Management, 1995
Proposes an information retrieval system based on a hybrid model consisting of an inductive learning and neural network system. Evaluates the system's responses to incomplete queries and inconsistent indexing. Query terms, discriminant descriptors, and the American Documentation Institute's query and document titles used in the evaluation are…
Descriptors: Indexing, Induction, Information Networks, Information Processing
