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Lazy learning

Known as: Lazy-learning 
In machine learning, lazy learning is a learning method in which generalization beyond the training data is delayed until a query is made to the… Expand
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Papers overview

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2010
2010
Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample… Expand
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Highly Cited
2008
Highly Cited
2008
Multilabel classification is a rapidly developing field of machine learning. Despite its short life, various methods for solving… Expand
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Highly Cited
2008
Highly Cited
2008
Associative classification is a promising technique to build accurate classifiers. However, in large or correlated data sets… Expand
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Highly Cited
2007
Highly Cited
2007
Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several… Expand
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Highly Cited
2004
Highly Cited
2004
Given a set of models and some training data, we would like to find the model that best describes the data. Finding the model… Expand
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Highly Cited
2004
Highly Cited
2004
The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence… Expand
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Highly Cited
2000
Highly Cited
2000
As opposed to traditional supervised learning, multiple-instance learning concerns the problem of classifying a bag of instances… Expand
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1997
1997
  • DesignGianluca Bontempi, Mauro Birattari, Hugues BersiniIridia
  • 1997
  • Corpus ID: 15638829
This paper presents local methods for modeling and control of discrete-time unknown nonlinear dynamical systems, when only a… Expand
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Review
1997
Review
1997
Many lazy learning algorithms are derivatives of the k-nearest neighbor (k-NN) classifier, which uses a distance function to… Expand
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Highly Cited
1996
Highly Cited
1996
Lazy learning algorithms, exemplified by nearest-neighbor algorithms, do not induce a concise hypothesis from a given training… Expand
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