Computational geometry: an introduction
Computational geometry: an introduction
Communications of the ACM - Special issue on parallelism
Acquisition of dynamic control knowledge for a robotic manipulator
Proceedings of the seventh international conference (1990) on Machine learning
Using local models to control movement
Advances in neural information processing systems 2
Bumptrees for efficient function, constraint, and classification learning
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Artificial Intelligence Review - Special issue on lazy learning
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
IGTree: Using Trees for Compression and Classification in Lazy LearningAlgorithms
Artificial Intelligence Review - Special issue on lazy learning
Colearning in Differential Games
Machine Learning
Accelerating exact k-means algorithms with geometric reasoning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An empirical analysis of techniques for constructing and searching k-dimensional trees
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting Sample-Data Distributions to Reduce the Cost of Nearest-Neighbor Searches with Kd-Trees
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Rapid detection of significant spatial clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast and robust short video clip search using an index structure
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Fast and robust video clip search using index structure
Proceedings of the 12th annual ACM international conference on Multimedia
New Algorithms for Efficient High-Dimensional Nonparametric Classification
The Journal of Machine Learning Research
Weighted Instance Typicality Search (WITS): A nearest neighbor data reduction algorithm
Intelligent Data Analysis
A Density-Based Data Reduction Algorithm for Robust Estimators
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Category detection using hierarchical mean shift
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-resolution learning for knowledge transfer
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
The anchors hierarchy: using the triangle inequality to survive high dimensional data
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Inducing models of behavior from expert task performance in virtual environments
Computational & Mathematical Organization Theory
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Instance-based learning methods explicitly remember all the data that they receive. They usually have no training phase and only at prediction time do they perform computation Then they take a query search the database for similar datapoints and build an on-line local model (such as a local average or local regression) with which to predict an output value. In this paper we review the advantages of instance based methods for autonomous systems but we also note the ensuing cost hopelessly slow computation as the database grows large. We present and evaluate a new way of structuring a database and a new algorithm for accessing it that maintains the advantages ot instance-based learning. Earlier attempts to combat the cost of instancebased learning have sacrificed the explicit retention of all data or been applicable only to instancebased predictions based on a small number of near neighbors, or have had to reintroduce an explicit training phase in the form of an interpolative data structure. Our approach builds a multiresolution data structure to summarize the database of experiences at all resolutions of interest simultaneously. This permits us to query the database with the same flexibility as a conventional linear search but at greatly reduced computational cost.