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Winnow (algorithm)

Known as: Winnow update, Winnow1, Winnow algorithm 
The winnow algorithm is a technique from machine learning for learning a linear classifier from labeled examples. It is very similar to the… 
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Related topics

Related topics

10 relations
C4.5 algorithmCRM114 (program)Linear classifierList of important publications in computer science

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2016
Highly Cited
2016
Online Passive-Aggressive Active learning
We investigate online active learning techniques for online classification tasks. Unlike traditional supervised learning… 
Highly Cited
2010
Highly Cited
2010
New Adaptive Algorithms for Online Classification
We propose a general framework to online learning for classification problems with time-varying potential functions in the… 
Highly Cited
2006
Highly Cited
2006
Winnowing Ontologies Based on Application Use
The requirements of specific applications and services are often over estimated when ontologies are reused or built. This… 
2005
2005
Filtering-Ranking Perceptron Learning for Partial Parsing
This work introduces a general phrase recognition system based on perceptrons, and a global online learning algorithm to train… 
Highly Cited
2003
Highly Cited
2003
tRuEcasIng
Truecasing is the process of restoring case information to badly-cased or non-cased text. This paper explores truecas-ing issues… 
Highly Cited
2002
Highly Cited
2002
Text Chunking based on a Generalization of Winnow
This paper describes a text chunking system based on a generalization of the Winnow algorithm. We propose a general statistical… 
1999
1999
On PAC learning using Winnow, Perceptron, and a Perceptron-like algorithm
In this paper we analyze the PAC learning abilities of several simple iterative algorithms for learning linear threshold… 
Highly Cited
1998
Highly Cited
1998
Merl A Mitsubishi Electric Research Laboratory
  • 1998
  • Corpus ID: 12283016
Highly Cited
1996
Highly Cited
1996
Testing sequential association: Estimating exact p values using sampled permutations.
Assigning p values to all transitions in a matrix based on their z scores is problematic on 2 counts: The z scores may not be… 
Highly Cited
1991
Highly Cited
1991
Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
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