Instance-based learning

Known as: IBL, Memory-based learning 
In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing… (More)
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Highly Cited
2009
Highly Cited
2009
Multilabel classification is an extension of conventional classification in which a single instance can be associated with… (More)
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Highly Cited
2004
Highly Cited
2004
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms… (More)
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2003
2003
A method of instance-based learning is introduced which makes use of possibility theory and fuzzy sets. Particularly, a… (More)
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Highly Cited
2000
Highly Cited
2000
Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the… (More)
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Highly Cited
1996
Highly Cited
1996
Several well-developed approaches to inductive learning low exist, but each has specific limitations that are hard to overcome… (More)
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Highly Cited
1995
Highly Cited
1995
This paper presents a new approach to inductive learning that combines aspects of instancebased learning and rule induction in a… (More)
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Review
1995
Review
1995
Instance-based learning methods explicitly remem­ ber all the data that they receive They usually have no training phase and only… (More)
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Highly Cited
1992
Highly Cited
1992
  • David W. Aha
  • International Journal of Man-Machine Studies
  • 1992
Incremental variants of the nearest neighbor algorithm are a potentially suitable choice for incremental learning tasks. They… (More)
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Highly Cited
1991
Highly Cited
1991
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms… (More)
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Highly Cited
1989
Highly Cited
1989
Several published reports show that instancebased learning algorithms yield high classification accuracies and have low storage… (More)
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