Machine learning for automatic mapping of planetary surfaces

Authors:
Tomasz F. Stepinski;Soumya Ghosh;Ricardo Vilalta
Affiliations:
Lunar and Planetary Institute, Houston, TX;Dept. of Computer Science, University of Colorado at Boulder, Boulder, CO;Dept. of Computer Science, University of Houston, Houston, TX
Venue:
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Year:
2007

Citing 12
Cited 3

C4.5: programs for machine learning

C4.5: programs for machine learning
The nature of statistical learning theory

The nature of statistical learning theory
Bagging predictors

Machine Learning
Stochastic Relaxation on Partitions With Connected Components and Its Application to Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Seeded Region Growing

IEEE Transactions on Pattern Analysis and Machine Intelligence
Color- and Texture-Based Image Segmentation Using EM and Its Application to Content-Based Image Retrieval

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Statistical Region Merging

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated classification of landforms on Mars

Computers & Geosciences
Automatic recognition of landforms on mars using terrain segmentation and classification

DS'06 Proceedings of the 9th international conference on Discovery Science

Machine Learning Tools for Automatic Mapping of Martian Landforms

IEEE Intelligent Systems
Optimization of scale and parametrization for terrain segmentation: An application to soil-landscape modeling

Computers & Geosciences
Improving bagging performance through multi-algorithm ensembles

PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

Analysis of postgenomic biological data (such as microarray and SNP data) is a subtle art and science, and the statistical methods most commonly utilized sometimes prove inadequate. Machine learning techniques can provide superior understanding in many ...