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- Publications
- Influence
Instance-Based Learning Algorithms
- D. Aha, D. Kibler, M. K. Albert
- Computer Science
- Machine Learning
- 3 January 1991
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed… Expand
Instance-based learning algorithms
- D. Aha, D. Kibler, M. K. Albert
- Machine Learning
- 2004
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed… Expand
A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms
- D. Wettschereck, D. Aha, T. Mohri
- Computer Science
- Artificial Intelligence Review
- 1 February 1997
Many lazy learning algorithms are derivatives of the k-nearest neighbor (k-NN) classifier, which uses a distance function to generate predictions from stored instances. Several studies have shown… Expand
Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms
- D. Aha
- Computer Science
- Int. J. Man Mach. Stud.
- 1 February 1992
Abstract Incremental variants of the nearest neighbor algorithm are a potentially suitable choice for incremental learning tasks. They have fast learning rates, low updating costs, and have recorded… Expand
A Comparative Evaluation of Sequential Feature Selection Algorithms
- D. Aha, R. L. Bankert
- Computer Science
- AISTATS
- 1995
Several recent machine learning publications demonstrate the utility of using feature selection algorithms in supervised learning tasks. Among these, sequential feature selection algorithms are… Expand
Generalizing from Case studies: A Case Study
- D. Aha
- Computer Science
- ML
- 1 July 1992
Abstract Most empirical evaluations of machine learning algorithms are case studies – evaluations of multiple algorithms on multiple databases. Authors of case studies implicitly or explicitly… Expand
Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game
- D. Aha, Matthew Molineaux, M. Ponsen
- Computer Science
- Künstliche Intell.
- 23 August 2005
While several researchers have applied case-based reasoning techniques to games, only Ponsen and Spronck (2004) have addressed the challenging problem of learning to win real-time games. Focusing on… Expand
A study of instance-based algorithms for supervised learning tasks: mathematica:l
- D. Aha
- Computer Science
- 1990
This dissertation introduces a framework for specifying instance-based algorithms that can solve supervised learning tasks. These algorithms input a sequence of instances and yield a partial concept… Expand
- 80
- 17
Simplifying decision trees: A survey
- Len Breslow, D. Aha
- Computer Science
- Knowl. Eng. Rev.
- 1997
Induced decision trees are an extensively-researched solution to classification tasks. For many practical tasks, the trees produced by tree-generation algorithms are not comprehensible to users due… Expand
Refining Conversational Case Libraries
- D. Aha, Len Breslow
- Computer Science
- ICCBR
- 25 July 1997
Conversational case-based reasoning (CBR) shells (e.g., Inference's CBR Express) are commercially successful tools for supporting the development of help desk and related applications. In contrast to… Expand