System identification: theory for the user
System identification: theory for the user
Employing linear regression in regression tree leaves
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Artificial Intelligence Review - Special issue on lazy learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Constructive incremental learning from only local information
Neural Computation
Using Model Trees for Classification
Machine Learning
Variable Resolution Discretization in Optimal Control
Machine Learning
On Estimating Probabilities in Tree Pruning
EWSL '91 Proceedings of the European Working Session on Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Functional Models for Regression Tree Leaves
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
SECRET: a scalable linear regression tree algorithm
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Top-Down Induction of Model Trees with Regression and Splitting Nodes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Incremental learning of linear model trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Qualitatively faithful quantitative prediction
Artificial Intelligence
Online classification of nonstationary data streams
Intelligent Data Analysis
Proceedings of the 24th international conference on Machine learning
Scalable look-ahead linear regression trees
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners
The Journal of Machine Learning Research
Learning Model Trees from Data Streams
DS '08 Proceedings of the 11th International Conference on Discovery Science
A Two-Level Model of Anticipation-Based Motor Learning for Whole Body Motion
Anticipatory Behavior in Adaptive Learning Systems
Learning model trees from evolving data streams
Data Mining and Knowledge Discovery
Incremental multi-target model trees for data streams
Proceedings of the 2011 ACM Symposium on Applied Computing
An evolutionary algorithm for global induction of regression trees with multivariate linear models
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Evolutionary optimized forest of regression trees: application in metallurgy
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Incremental linear model trees on massive datasets: keep it simple, keep it fast
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Multivariate convex regression with adaptive partitioning
The Journal of Machine Learning Research
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A linear model tree is a decision tree with a linear functional model in each leaf. Previous model tree induction algorithms have been batch techniques that operate on the entire training set. However there are many situations when an incremental learner is advantageous. In this article a new batch model tree learner is described with two alternative splitting rules and a stopping rule. An incremental algorithm is then developed that has many similarities with the batch version but is able to process examples one at a time. An online pruning rule is also developed. The incremental training time for an example is shown to only depend on the height of the tree induced so far, and not on the number of previous examples. The algorithms are evaluated empirically on a number of standard datasets, a simple test function and three dynamic domains ranging from a simple pendulum to a complex 13 dimensional flight simulator. The new batch algorithm is compared with the most recent batch model tree algorithms and is seen to perform favourably overall. The new incremental model tree learner compares well with an alternative online function approximator. In addition it can sometimes perform almost as well as the batch model tree algorithms, highlighting the effectiveness of the incremental implementation.