ACM Computing Surveys (CSUR)
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-Theoretic Techniques for Web Content Mining
Graph-Theoretic Techniques for Web Content Mining
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graph-Theoretic Approach to Enterprise Network Dynamics (Progress in Computer Science and Applied Logic (PCS))
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Kernel k-Means Clustering Applied to Vector Space Embeddings of Graphs
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
Classifier ensembles for vector space embedding of graphs
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Kernels For Structured Data
Transforming strings to vector spaces using prototype selection
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Combining Local and Global KNN With Cotraining
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Semi-supervised dependency parsing using generalized tri-training
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Combining committee-based semi-supervised learning and active learning
Journal of Computer Science and Technology
A novel multi-view learning developed from single-view patterns
Pattern Recognition
Semi-supervised classification based on random subspace dimensionality reduction
Pattern Recognition
Time-aware co-training for indoors localization in visual lifelogs
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Unsupervised Weight Parameter Estimation Method for Ensemble Learning
Journal of Mathematical Modelling and Algorithms
Sample-based software defect prediction with active and semi-supervised learning
Automated Software Engineering
Semi-supervised ensemble classification in subspaces
Applied Soft Computing
Exploiting unlabeled data to enhance ensemble diversity
Data Mining and Knowledge Discovery
Information Sciences: an International Journal
| Hi-index | 0.00 |
Semi-supervised learning and ensemble learning are two important learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; the latter attempts to achieve strong generalization by using multiple learners. In this paper we advocate generating stronger learning systems by leveraging unlabeled data and classifier combination.