On the exponential value of labeled samples
Pattern Recognition Letters
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Improving Short-Text Classification using Unlabeled Data for Classification Problems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Unlabeled Data Can Degrade Classification Performance of Generative Classifiers
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Toward efficient collaborative classification for distributed video surveillance
Toward efficient collaborative classification for distributed video surveillance
Gene functional classification by semi-supervised learning from heterogeneous data
Proceedings of the 2003 ACM symposium on Applied computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Multimodal integration-a statistical view
IEEE Transactions on Multimedia
An Extension of the TIGR M4 Suite to Preprocess and Visualize Affymetrix Binary Files
Computational Intelligence Methods for Bioinformatics and Biostatistics
Improving gene selection in microarray data analysis using fuzzy patterns inside a CBR system
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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Machine learning and data mining have found a multitude of successful applications in microarray analysis, with gene clustering and classification of tissue samples being widely cited examples. Low-level microarray analysis -- often associated with the pre-processing stage within the microarray life-cycle -- has increasingly become an area of active research, traditionally involving techniques from classical statistics. This paper explores opportunities for the application of machine learning and data mining methods to several important low-level microarray analysis problems: monitoring gene expression, transcript discovery, genotyping and resequencing. Relevant methods and ideas from the machine learning community include semi-supervised learning, learning from heterogeneous data, and incremental learning.