Algorithms for clustering data
Algorithms for clustering data
Computer processing of remotely-sensed images: an introduction
Computer processing of remotely-sensed images: an introduction
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Semi-Supervised Self-Training of Object Detection Models
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Co-training for predicting emotions with spoken dialogue data
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
The Journal of Machine Learning Research
Semi-Supervised Learning
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
A New Convergence Proof of Fuzzy c-Means
IEEE Transactions on Fuzzy Systems
Local linear perceptrons for classification
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Combining active learning and semi-supervised learning to construct SVM classifier
Knowledge-Based Systems
DTW-D: time series semi-supervised learning from a single example
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Computers and Electrical Engineering
A self-trained semisupervised SVM approach to the remote sensing land cover classification
Computers & Geosciences
A survey of multiple classifier systems as hybrid systems
Information Fusion
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In this article, we present a semisupervised support vector machine that uses self-training approach. We then construct an ensemble of semisupervised SVM classifiers to address the problem of pixel classification of remote sensing images. Semisupervised support vector machines (S^3VMs) are based on applying the margin maximization principle to both labeled and unlabeled samples. The ensemble of SVM classifiers recognizes the conceptual similarity between component classifiers from the same data source. The effectiveness of the proposed technique is first demonstrated for two numeric remote sensing data described in terms of feature vectors and then identifying different land cover regions in remote sensing imagery. Experimental results on these datasets show that employing this learning scheme can increase the accuracy level. The performance of the ensemble is compared with one of its component classifier and conventional SVM in terms of accuracy and quantitative cluster validity indices.