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Inductive bias

Known as: Learning bias 
The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs given… 
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Related topics

Related topics

11 relations
Cross-validation (statistics)Feature selectionK-nearest neighbors algorithmMeta learning (computer science)

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2013
2013
Fast and Effective Single Pass Bayesian Learning
The rapid growth in data makes ever more urgent the quest for highly scalable learning algorithms that can maximize the benefit… 
2007
2007
In Search Of Articulated Attractors
Recurrent attractor networks offer many advantages over feedforward networks for the modeling of psychological phenomena. Their… 
2005
2005
Selecting feature subsets for inducing classifiers using a committee of heterogeneous methods
As a previous step to machine learning (ML) induced classifiers, attribute subset selection methods have become an efficient… 
2005
2005
Heterogeneous Attribute Utility Model: A new approach for modeling user profiles for recommendation systems
Reference LIA-CONF-2006-003 URL: http://db.cs.ualberta.ca/webkdd05/ Record created on 2006-05-19, modified on 2017-05-12 
2005
2005
Converting Semantic Meta-knowledge into Inductive Bias
The Cyc KB has a rich pre-existing ontology for representing common sense knowledge. To clarify and enforce its terms' semantics… 
2004
2004
MULTIPLE CLASSIFIER COMBINATION THROUGH ENSEMBLES AND DATA GENERATION
An ensemble of classifiers consists of a set of individually trained classifiers whose predictions are combined when classifying… 
2000
2000
Cumulativity as Inductive Bias
An important di erence in inductive bias between machine learning approaches is whether they assume the e ects of di erent… 
1998
1998
On the development of inductive learning algorithms: generating flexible and adaptable concept representations
Vast amount of research in machine learning has focused on creating new algorithms stemming from reenements to existing learning… 
1996
1996
Representation and Induction of Finite State Machines using Time-Delay Neural Networks
This work investigates the representational and inductive capabilities of time-delay neural networks (TDNNs) in general, and of… 
1996
1996
Relational Instance-based Learning { an Initial Case Study
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