Active bias adjustment for incremental, supervised concept learning
Active bias adjustment for incremental, supervised concept learning
Neural networks and the bias/variance dilemma
Neural Computation
Original Contribution: Stacked generalization
Neural Networks
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Evaluation and Selection of Biases in Machine Learning
Machine Learning - Special issue on bias evaluation and selection
Recursive Automatic Bias Selection for Classifier Construction
Machine Learning - Special issue on bias evaluation and selection
Learning in the presence of concept drift and hidden contexts
Machine Learning
On the Accuracy of Meta-learning for Scalable Data Mining
Journal of Intelligent Information Systems
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on inductive transfer
Theoretical models of learning to learn
Learning to learn
Learning to learn
Arbitrating among competing classifiers using learned referees
Knowledge and Information Systems
Machine Learning - Special issue on inductive transfer
A perspective view and survey of meta-learning
Artificial Intelligence Review
Combining Classifiers with Meta Decision Trees
Machine Learning
Neural Computation
God Doesn't Always Shave with Occam's Razor - Learning When and How to Prune
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Characterization of Classification Algorithms
EPIA '95 Proceedings of the 7th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Generalizing from Case studies: A Case Study
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
Optimal Ordered Problem Solver
Machine Learning
On Data and Algorithms: Understanding Inductive Performance
Machine Learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Extracting constraints for process modeling
Proceedings of the 4th international conference on Knowledge capture
Mindful: A framework for Meta-INDuctive neuro-FUzzy Learning
Information Sciences: an International Journal
Learning to learn implicit queries from gaze patterns
Proceedings of the 25th international conference on Machine learning
Selective generation of training examples in active meta-learning
International Journal of Hybrid Intelligent Systems - HIS 2007
Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Acquiring Rules for Rules: Neuro-Dynamical Systems Account for Meta-Cognition
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Using Supervised Complexity Measures in the Analysis of Cancer Gene Expression Data Sets
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Active Generation of Training Examples in Meta-Regression
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Active learning to support the generation of meta-examples
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Evaluating learning algorithms and classifiers
International Journal of Intelligent Information and Database Systems
Algorithm selection as a bandit problem with unbounded losses
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Privacy leakage in multi-relational learning via unwanted classification models
Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research
Meta-learning experiences with the mindful system
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
A machine learning approach to define weights for linear combination of forecasts
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Meta-data: characterization of input features for meta-learning
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Learning the bias of a classifier in a GA-Based inductive learning environment
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
A landmarker selection algorithm based on correlation and efficiency criteria
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
SRF: a framework for the study of classifier behavior under training set mislabeling noise
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Clustering algorithm recommendation: a meta-learning approach
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Reducing the size of databases for multirelational classification: a subgraph-based approach
Journal of Intelligent Information Systems
| Hi-index | 0.00 |
Recent advances in meta-learning are providing the foundations to construct meta-learning assistants and task-adaptive learners. The goal of this special issue is to foster an interest in meta-learning by compiling representative work in the field. The contributions to this special issue provide strong insights into the construction of future meta-learning tools. In this introduction we present a common frame of reference to address work in meta-learning through the concept of meta-knowledge. We show how meta-learning can be simply defined as the process of exploiting knowledge about learning that enables us to understand and improve the performance of learning algorithms.