C4.5: programs for machine learning
C4.5: programs for machine learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Diversity versus Quality in Classification Ensembles Based on Feature Selection
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Efficient Feature Selection in Conceptual Clustering
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Comparison of Incremental Case-Based Reasoning and Inductive Learning
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
Dimensionality Reduction of Unsupervised Data
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Sequential Diagnosis in the Independence Bayesian Framework
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Exploiting Taxonomic and Causal Relations in Conversational Case Retrieval
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Precision and Recall in Interactive Case-Based Reasoning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A Similarity-Based Approach to Attribute Selection in User-Adaptive Sales Dialogs
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A Case-Based Reasoning View of Automated Collaborative Filtering
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
An Analysis of Research Themes in the CBR Conference Literature
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
CBR for CBR: A Case-Based Template Recommender System for Building Case-Based Systems
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Supporting conversation variability in COBBER using causal loops
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
A knowledge-intensive method for conversational CBR
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Improving the performance of recommender systems that use critiquing
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Completeness criteria for retrieval in recommender systems
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Evaluating CBR systems using different data sources: a case study
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Decision diagrams: fast and flexible support for case retrieval and recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Semi-automatic generation of recommendation processes and their GUIs
Proceedings of the 2013 international conference on Intelligent user interfaces
Streamlining user interaction in tag-based conversational navigation of knowledge resource libraries
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
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Online decision guides typically ask too many questions of the user, as they make no attempt to focus the questions. We describe some approaches to minimising the questions asked of a user in an online query situation. Questions are asked in an order that reflects their ability to narrow down the set of cases. Thus time to reach an answer is decreased. This has the dual benefit of taking some of the monotony out of online queries, and also of decreasing the amount of network request-response cycles. Most importantly, question order is decided at run time, and therefore adapts to the user. This approach is in the spirit of lazy learning with induction delayed to run-time, allowing adaptation to the emerging details of the situation. We evaluate a few different approaches to the question selection task, and compare the best approach (one based on ideas from retrieval in CBR) to a commercial online decision guide.