Information-based objective functions for active data selection
Neural Computation
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning in graphical models
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Influence-based model decomposition
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Influence-based model decomposition for reasoning about spatially distributed physical systems
Artificial Intelligence
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Sampling Strategies for Mining in Data-Scarce Domains
Computing in Science and Engineering
STA: Spatio-Temporal Aggregation with Applications to Analysis of Diffusion-Reaction Phenomena
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
Active learning with statistical models
Journal of Artificial Intelligence Research
Spatial aggregation: theory and applications
Journal of Artificial Intelligence Research
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Spatial aggregation: language and applications
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A Randomness Based Analysis on the Data Size Needed for Removing Deceptive Patterns
IEICE - Transactions on Information and Systems
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Multi-level spatial aggregates are important for data mining in a variety of scientific and engineering applications, from analysis of weather data (aggregating temperature and pressure data into ridges and fronts) to performance analysis of wireless systems (aggregating simulation results into configuration space regions exhibiting particular performance characteristics). In many of these applications, data collection is expensive and time consuming, so effort must be focused on gathering samples at locations that will be most important for the analysis. This requires that we be able to functionally model a data mining algorithm in order to assess the impact of potential samples on the mining of suitable spatial aggregates. This paper describes a novel Gaussian process approach to modeling multi-layer spatial aggregation algorithms, and demonstrates the ability of the resulting models to capture the essential underlying qualitative behaviors of the algorithms. By helping cast classical spatial aggregation algorithms in a rigorous quantitative framework, the Gaussian process models support diverse uses such as directed sampling, characterizing the sensitivity of a mining algorithm to particular parameters, and understanding how variations in input data fields percolate up through a spatial aggregation hierarchy.