Communications of the ACM - Special issue on parallelism
Incremental, instance-based learning of independent and graded concept descriptions
Proceedings of the sixth international workshop on Machine learning
Using local models to control movement
Advances in neural information processing systems 2
Neural networks and the bias/variance dilemma
Neural Computation
Original Contribution: Stacked generalization
Neural Networks
Practical neural network recipes in C++
Practical neural network recipes in C++
Case-based reasoning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Fast and simple scatterplot smoothing
Computational Statistics & Data Analysis
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Artificial Intelligence Review - Special issue on lazy learning
The Racing Algorithm: Model Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Lazy learning meets the recursive least squares algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
A model selection approach for local learning
AI Communications - Special issue on AI research in the Benelux
The local paradigm for modeling and control: from neuro-fuzzy to lazy learning
Fuzzy Sets and Systems - Special issue on formal methods for fuzzy modeling and control
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Artificial Intelligence Review - Special issue on lazy learning
Recursive Lazy Learning for Modeling and Control
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Local Learning for Iterated Time-Series Prediction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A study of instance-based algorithms for supervised learning tasks: mathematical, empirical, and psychological evaluations
The Journal of Machine Learning Research
Grasp recognition for uncalibrated data gloves: A machine learning approach
Presence: Teleoperators and Virtual Environments
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The traditional approach to supervised learning is global modeling which describes the relationship between the input and the output with an analytical function over the whole input domain. What makes global modeling appealing is the nice property that even for huge datasets, a parametric model requires a short program that can be executed in a reduced amount of time.