• Publications
  • Influence
Instance-Based Learning Algorithms
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributedExpand
  • 3,264
  • 303
Instance-based learning algorithms
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributedExpand
  • 1,287
  • 72
A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms
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 shownExpand
  • 731
  • 34
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 recordedExpand
  • 425
  • 33
A Comparative Evaluation of Sequential Feature Selection Algorithms
Several recent machine learning publications demonstrate the utility of using feature selection algorithms in supervised learning tasks. Among these, sequential feature selection algorithms areExpand
  • 399
  • 28
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 explicitlyExpand
  • 265
  • 23
Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game
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 onExpand
  • 211
  • 17
A study of instance-based algorithms for supervised learning tasks: mathematica:l
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 conceptExpand
  • 80
  • 17
Simplifying decision trees: A survey
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 dueExpand
  • 284
  • 13
Refining Conversational Case Libraries
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 toExpand
  • 167
  • 13