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)
Wikipedia

Related topics

Related topics

13 relations
Analogical modelingBias–variance tradeoffClinical decision support systemDeep learning
(More)

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2009
Highly Cited
2009
Combining instance-based learning and logistic regression for multilabel classification
Multilabel classification is an extension of conventional classification in which a single instance can be associated with… (More)
  • table 1
  • table 2
  • figure 1
  • table 3
Is this relevant?
Highly Cited
2004
Highly Cited
2004
Instance-based learning algorithms
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
2003
2003
Possibilistic instance-based learning
A method of instance-based learning is introduced which makes use of possibility theory and fuzzy sets. Particularly, a… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
Is this relevant?
Highly Cited
2000
Highly Cited
2000
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the… (More)
  • table 2
  • table 3
  • table 1
  • table 4
  • figure 1
Is this relevant?
Highly Cited
1996
Highly Cited
1996
Unifying instance-based and rule-based induction
Several well-developed approaches to inductive learning low exist, but each has specific limitations that are hard to overcome… (More)
  • table 4
  • table 5
  • table 6
  • table 7
  • table 8
Is this relevant?
Highly Cited
1995
Highly Cited
1995
Rule Induction and Instance-Based Learning: A Unified Approach
This paper presents a new approach to inductive learning that combines aspects of instancebased learning and rule induction in a… (More)
  • table 3
  • table 4
Is this relevant?
Review
1995
Review
1995
Multiresolution Instance-Based Learning
Instance-based learning methods explicitly remem­ ber all the data that they receive They usually have no training phase and only… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 6
Is this relevant?
Highly Cited
1992
Highly Cited
1992
Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms
  • 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)
  • table 1
  • table 2
  • table 3
  • figure 1
  • figure 2
Is this relevant?
Highly Cited
1991
Highly Cited
1991
Instance-Based Learning Algorithms
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms… (More)
Is this relevant?
Highly Cited
1989
Highly Cited
1989
Noise-Tolerant Instance-Based Learning Algorithms
Several published reports show that instancebased learning algorithms yield high classification accuracies and have low storage… (More)
  • table 4
Is this relevant?