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… (More)
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Topic mentions per year

Topic mentions per year

1994-2018
0102019942018

Papers overview

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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… (More)
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2002
2002
Naive Bayes is a probability-based classification method which is based on the assumption that attributes are conditionally… (More)
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Highly Cited
2000
Highly Cited
2000
The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence… (More)
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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… (More)
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1998
1998
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction interpolating locally the… (More)
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Highly Cited
1997
Highly Cited
1997
This paper presents local methods for modeling and control of discrete-time unknown nonlinear dynamical systems, when only a… (More)
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Highly Cited
1997
Highly Cited
1997
We describe the IGTree learning algorithm, which compresses an instance base into a tree structure. The concept of information… (More)
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Highly Cited
1997
Highly Cited
1997
Given a set of models and some training data, we would like to find the model that best describes the data. Finding the model… (More)
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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… (More)
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Highly Cited
1996
Highly Cited
1996
Lazy learning algorithms, exemplified by nearestneighbor algorithms, do not induce a concise hypothesis from a given training set… (More)
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