Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
Spatial Databases-Accomplishments and Research Needs
IEEE Transactions on Knowledge and Data Engineering
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A bibliography of temporal, spatial and spatio-temporal data mining research
ACM SIGKDD Explorations Newsletter
Clustering spatial data with a hybrid EM approach
Pattern Analysis & Applications
A Semi-Supervised Learning Method for Remote Sensing Data Mining
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Spatial contextual classification and prediction models for mining geospatial data
IEEE Transactions on Multimedia
*Miner: a spatial and spatiotemporal data mining system
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
STPMiner: a highperformance spatiotemporal pattern mining toolbox
Proceedings of the 2nd international workshop on Petascal data analytics: challenges and opportunities
Spatiotemporal data mining in the era of big spatial data: algorithms and applications
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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We began by developing a semi-supervised learning method based on the expectation-maximization (EM) algorithm, and maximum likelihood and maximum a posteriori classifiers (MLC and MAP). This scheme utilizes a small set of labeled and a large number of unlabeled training samples. We conducted several experiments on multi-spectral images to understand the impact of unlabeled samples on the classification performance. Our study shows that although, in general, classification accuracy improves with the addition of unlabeled training samples, it is not guaranteed to achieve consistently higher accuracies unless sufficient care is exercised when designing a semi-supervised classifier. We also extended this semi-supervised framework to model spatial context through Markov random fields (MRF). Initial experiments showed an improved accuracy of the spatial semi-supervised algorithm (SSSL) over MLC, semi-supervised, and MRF classifiers. An efficient implementation is provided so that the SSSL can be applied in production environments. We also discuss some open research problems.