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Meta learning (computer science)

Meta learning is a subfield of Machine learning where automatic learning algorithms are applied on meta-data about machine learning experiments… 
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Related topics

Related topics

12 relations
Algorithm SelectionCase-based reasoningComplexityConstraint satisfaction

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2020
2020
Optimizing Correlated Graspability Score and Grasp Regression for Better Grasp Prediction
Grasping objects is one of the most important abilities to master for a robot in order to interact with its environment. Current… 
2019
2019
VIABLE: Fast Adaptation via Backpropagating Learned Loss
In few-shot learning, typically, the loss function which is applied at test time is the one we are ultimately interested in… 
2019
2019
Meta-Learning-Based Deep Learning Model Deployment Scheme for Edge Caching
Recently, with big data and high computing power, deep learning models have achieved high accuracy in prediction problems… 
2018
2018
Rethink and Redesign Meta learning
Recently, meta-learning has shown as a promising way to improve the ability to learn from few-data for many computer vision tasks… 
2013
2013
Kollaboratives Planen und Lernen mit der web-basierten Lernplattform Metafora
In diesem Artikel prasentieren wir das Metafora-Projekt, das Gruppen von Schulern bei der Bearbeitung von Problemstellungen im… 
2011
2011
Meta-learning for Selecting a Multi-label Classification Algorithm
Although various algorithms for multi-label classification have been developed in recent years, there is little, if any… 
2010
2010
A Genetic Programming Approach for Software Reliability Modeling
Genetic programming (GP) models adapt better to the reliability curve when compared with other traditional, and non-parametric… 
2008
2008
Perspectives on Team Dynamics: Meta Learning and Systems Intelligence
Losada observed management teams develop their annual strategic plans in a lab designed for studying team behaviour. Based on… 
Review
2004
Review
2004
From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms
Separate-and-conquer or covering rule learning algorithms may be viewed as a technique for using local pattern discovery for… 
2001
2001
On the Comparison of Inductive Inference Criteria for Uniform Learning of Finite Classes
We consider a learning model in which each element of a class of recursive functions is to be identified in the limit by a… 
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