-
Mazzucchelli, A.; Sartori , F.: String similarity in CBR platforms : a preliminary study (2014)
0.06
0.057698686 = product of:
0.11539737 = sum of:
0.11539737 = sum of:
0.06581766 = weight(_text_:research in 1568) [ClassicSimilarity], result of:
0.06581766 = score(doc=1568,freq=8.0), product of:
0.1491455 = queryWeight, product of:
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.05227703 = queryNorm
0.44129837 = fieldWeight in 1568, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.0546875 = fieldNorm(doc=1568)
0.04957971 = weight(_text_:22 in 1568) [ClassicSimilarity], result of:
0.04957971 = score(doc=1568,freq=2.0), product of:
0.18306525 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05227703 = queryNorm
0.2708308 = fieldWeight in 1568, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0546875 = fieldNorm(doc=1568)
0.5 = coord(1/2)
- Abstract
- Case Based Reasoning is a very important research trend in Artificial Intelligence and can be a powerful approach in the solution of complex problems characterized by heterogeneous knowledge. In this paper we present an ongoing research project where CBR is exploited to support the identification of enterprises potentially going to bankruptcy, through a comparison of their balance indexes with the ones of similar and already closed firms. In particular, the paper focuses on how developing similarity measures for strings can be profitably supported by metadata models of case structures and semantic methods like Query Expansion.
- Pages
- S.22-29
- Source
- Metadata and semantics research: 8th Research Conference, MTSR 2014, Karlsruhe, Germany, November 27-29, 2014, Proceedings. Eds.: S. Closs et al
-
Ram, A.; Santamaria, J.C.: Continuous case-based reasoning (1997)
0.05
0.04713631 = product of:
0.09427262 = sum of:
0.09427262 = sum of:
0.037610095 = weight(_text_:research in 435) [ClassicSimilarity], result of:
0.037610095 = score(doc=435,freq=2.0), product of:
0.1491455 = queryWeight, product of:
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.05227703 = queryNorm
0.2521705 = fieldWeight in 435, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.0625 = fieldNorm(doc=435)
0.056662526 = weight(_text_:22 in 435) [ClassicSimilarity], result of:
0.056662526 = score(doc=435,freq=2.0), product of:
0.18306525 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05227703 = queryNorm
0.30952093 = fieldWeight in 435, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=435)
0.5 = coord(1/2)
- Abstract
- Introduces a new method for continuous case-based reasoning, and discusses its applications to the dynamic selection, modification and acquisition of robot bahaviours in an autonomous navigation system, SINS (self-improving navigation system): The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. Discusses case-based reasoning issues addressed by this research
- Date
- 6. 3.1997 16:22:15
-
Akerele, O.; David, A.; Osofisan, A.: Using the concepts of Case Based Reasoning and Basic Categories for enhancing adaptation to the user's level of knowledge in Decision Support System (2014)
0.04
0.035352234 = product of:
0.07070447 = sum of:
0.07070447 = sum of:
0.028207572 = weight(_text_:research in 1449) [ClassicSimilarity], result of:
0.028207572 = score(doc=1449,freq=2.0), product of:
0.1491455 = queryWeight, product of:
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.05227703 = queryNorm
0.18912788 = fieldWeight in 1449, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.046875 = fieldNorm(doc=1449)
0.042496894 = weight(_text_:22 in 1449) [ClassicSimilarity], result of:
0.042496894 = score(doc=1449,freq=2.0), product of:
0.18306525 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05227703 = queryNorm
0.23214069 = fieldWeight in 1449, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=1449)
0.5 = coord(1/2)
- Abstract
- In most search systems, mapping queries with documents employs techniques such as vector space model, naïve Bayes, Bayesian theorem etc. to classify resulting documents. In this research studies, we are proposing the use of the concept of basic categories to representing the user's level of knowledge based on the concepts he employed during his search activities, so that the system could propose adapted results based on the observed user's level of knowledge. Our hypothesis is that this approach will enhance the decision support system for solving decisional problems in which information retrieval constitutes the backbone technical problem.
- Source
- Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
-
Golding, A.R.; Rosenbloom, P.S.: Improving accuracy by combining rule-based and case-based reasoning (1996)
0.01
0.0141656315 = product of:
0.028331263 = sum of:
0.028331263 = product of:
0.056662526 = sum of:
0.056662526 = weight(_text_:22 in 607) [ClassicSimilarity], result of:
0.056662526 = score(doc=607,freq=2.0), product of:
0.18306525 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05227703 = queryNorm
0.30952093 = fieldWeight in 607, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=607)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 6. 3.1997 16:22:15
-
Kohno, T.: Error repair and knowledge acquisition via case-based reasoning (1997)
0.01
0.0141656315 = product of:
0.028331263 = sum of:
0.028331263 = product of:
0.056662526 = sum of:
0.056662526 = weight(_text_:22 in 437) [ClassicSimilarity], result of:
0.056662526 = score(doc=437,freq=2.0), product of:
0.18306525 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05227703 = queryNorm
0.30952093 = fieldWeight in 437, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=437)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 6. 3.1997 16:22:15
-
Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009)
0.01
0.010624223 = product of:
0.021248447 = sum of:
0.021248447 = product of:
0.042496894 = sum of:
0.042496894 = weight(_text_:22 in 3051) [ClassicSimilarity], result of:
0.042496894 = score(doc=3051,freq=2.0), product of:
0.18306525 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.05227703 = queryNorm
0.23214069 = fieldWeight in 3051, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=3051)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 22. 8.2009 19:51:28
-
Mahapatra, R.; Sen, A.: Case base management systems : providing database support to case-based reasoners (1994)
0.01
0.009402524 = product of:
0.018805047 = sum of:
0.018805047 = product of:
0.037610095 = sum of:
0.037610095 = weight(_text_:research in 525) [ClassicSimilarity], result of:
0.037610095 = score(doc=525,freq=2.0), product of:
0.1491455 = queryWeight, product of:
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.05227703 = queryNorm
0.2521705 = fieldWeight in 525, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.0625 = fieldNorm(doc=525)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Case based reasoning offers a new approach for developing knowledge based systems. Most systems are currently prototypes. A number of research issues need to be resolved to facilitate the transition of these prototypes to large application systems, the primary issue being to develop data management support for these prototypes. Analyzes this data management support and proposes a new concept called a casease management system to perfom data management functions for case based systems
-
Ozturk, P.; Aamodt, A.: ¬A context model for knowledge-intensive case-based reasoning (1998)
0.01
0.009402524 = product of:
0.018805047 = sum of:
0.018805047 = product of:
0.037610095 = sum of:
0.037610095 = weight(_text_:research in 3844) [ClassicSimilarity], result of:
0.037610095 = score(doc=3844,freq=2.0), product of:
0.1491455 = queryWeight, product of:
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.05227703 = queryNorm
0.2521705 = fieldWeight in 3844, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.0625 = fieldNorm(doc=3844)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Reports on research which studied how the incorporation of case-specific, episodic, knowledge enables decision-support systems to become more robust and to adapt to a changing environment by continuously retaining new problem-solving cases as they occur during normal system operation
-
Ress, D.A.; Young, R.E.: ¬A distributed fuzzy constraint satisfaction system with context-based reasoning (1998)
0.01
0.008227208 = product of:
0.016454415 = sum of:
0.016454415 = product of:
0.03290883 = sum of:
0.03290883 = weight(_text_:research in 3842) [ClassicSimilarity], result of:
0.03290883 = score(doc=3842,freq=2.0), product of:
0.1491455 = queryWeight, product of:
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.05227703 = queryNorm
0.22064918 = fieldWeight in 3842, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.0546875 = fieldNorm(doc=3842)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Presents a fuzzy constraint satisfaction system which can be used in a distributive environment where large problems can be broken down into smaller constraint networks for easier processing. Identifies contexts which exist within the constraint satisfaction system. Context based reasoning is identified both within and among constraint networks. Outlines the motviation behind the research and describes the fuzzy constraint satisfaction system FuzCon. Points out 3 mappings of the context-based reasoning 'ist' operator to fuzzy constraints and presents an example of designing a printed wiring board
-
He, W.; Erdelez, S.; Wang, F.-K.; Shyu, C.-R.: ¬The effects of conceptual description and search practice on users' mental models and information seeking in a case-based reasoning retrieval system (2008)
0.01
0.005876578 = product of:
0.011753156 = sum of:
0.011753156 = product of:
0.023506312 = sum of:
0.023506312 = weight(_text_:research in 2036) [ClassicSimilarity], result of:
0.023506312 = score(doc=2036,freq=2.0), product of:
0.1491455 = queryWeight, product of:
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.05227703 = queryNorm
0.15760657 = fieldWeight in 2036, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.8529835 = idf(docFreq=6931, maxDocs=44218)
0.0390625 = fieldNorm(doc=2036)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- This paper reportes a study that investigated the effects of conceptual description and search practice on users' mental models and information seeking in a case-based reasoning retrieval (CBR) system with a best match search mechanism. This study also found examined how the presence of a mental model affects the users' search performance and satisfaction in this system. The results of this study revealed that the conceptual description and search practice treatments do not have significantly different effects on the types of user's mental models, search correctness, and search satisfaction. However, the search practice group spent significantly less time than the conceptual description group in finding the results. Qualitative analysis for the subjects' post mental models revealed that subjects in the conceptual description group seem to have more complete mental models of the best match system than those in the search practice group. This study also that subjects with the best match mental models have significantly higher search correctness and search result satisfaction than subjects without the best match mental models. However, the best match mental models do not guarantee less search time in finding the results. This study did not find a significant correlation among search time, search correctness and search satisfaction. The study concludes with suggestions for future research and implications for system developers who are interested in CBR retrieval systems.
