Instance-based learning algorithms
- D. Aha, D. Kibler, M. K. Albert
- Computer ScienceMachine-mediated learning
- 2004
This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.
Instance-Based Learning Algorithms
- D. Aha, D. Kibler, M. K. Albert
- Computer ScienceMachine-mediated learning
- 3 January 1991
This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.
A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms
- D. Wettschereck, D. Aha, T. Mohri
- Computer ScienceArtificial Intelligence Review
- 1 February 1997
A class of weight-setting methods for lazy learning algorithms which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less pre-processing, perform better in the presence of interacting features, and generally require less training data to learn good settings.
Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms
- D. Aha
- Computer ScienceInt. J. Man Mach. Stud.
- 1 February 1992
A Comparative Evaluation of Sequential Feature Selection Algorithms
- D. Aha, R. Bankert
- Computer ScienceInternational Conference on Artificial…
- 1995
Positive empirical results are reported on variants of sequential feature selection that might be more appropriate for some performance tasks, and it is argued for their serious consideration in similar learning tasks.
Generalizing from Case studies: A Case Study
- D. Aha
- Computer ScienceML Workshop
- 1 July 1992
INSTANCE-BASED LEARNING : Nearest Neighbour with Generalisation
- S. Salzberg, D. Aha, R. Holte
- Computer Science
- 1995
The results show that non-nested generalisation of exemplars improves the classification performance of nearest neighbour systems and reduces classification time.
Conversational Case-Based Reasoning
- D. Aha, Len Breslow, Hector Muñoz-Avila
- Computer ScienceApplied intelligence (Boston)
- 7 February 2001
This work highlights important CCBR problems, evaluates approaches for solving them, and suggest alternatives to be considered for future research.
Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game
- D. Aha, M. Molineaux, M. Ponsen
- Computer ScienceKünstliche Intell.
- 23 August 2005
A plan retrieval algorithm is introduced that, by using three key sources of domain knowledge, removes the assumption of a static opponent and significantly outperforms the best among a set of genetically evolved plans when tested against random Wargus opponents.
Refining Conversational Case Libraries
- D. Aha, Len Breslow
- Computer ScienceInternational Conference on Case-Based Reasoning
- 25 July 1997
This work describes an approach for revising case libraries according to design guidelines, its implementation in Clire, and empirical results showing that, under some conditions, this approach can improve conversational CBR performance.
...
...