@Article{aha91a,
  author =	 {David W. Aha and Dennis Kibler and Marc K. Albert},
  title =	 {Instance-Based Learning Algorithms},
  googleid =	 {CKC64N7thR4J:scholar.google.com/},
  journal =	 {Machine Learning},
  year =	 1991,
  volume =	 6,
  number =	 1,
  pages =	 {33--66},
  abstract =	 {Storing and using specific instances improves the
                  performance of several supervised learning
                  algorithms. These include algorithms that learn
                  decision trees, classification rules, and
                  distributed networks. However, no investigation has
                  analyzed algorithms that use only specific instances
                  to solve incremental learning tasks. In this paper,
                  we describe a framework and methodology, called
                  instance-based learning, that generates
                  classification predictions using only specific
                  instances. Instance-based learning algorithms do not
                  maintain a set of abstractions derived from specific
                  instances. This approach extends the nearest
                  neighbor algorithm, which has large storage
                  requirements. We describe how storage requirements
                  can be significantly reduced with, at most, minor
                  sacrifices in learning rate and classification
                  accuracy. While the storage-reducing algorithm
                  performs well on several realworld databases, its
                  performance degrades rapidly with the level of
                  attribute noise in training instances. Therefore, we
                  extended it with a significance test to distinguish
                  noisy instances. This extended algorithm's
                  performance degrades gracefully with increasing
                  noise levels and compares favorably with a
                  noise-tolerant decision tree algorithm.},
  keywords =     {ai learning},
  doi =		 {10.1023/A:1022689900470},
  cluster = 	 {2199425534549205000}
}