Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A scalable, incremental learning algorithm for classification problems
Computers and Industrial Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning Vector Quantization with Training Data Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust fuzzy relational classifier incorporating the soft class labels
Pattern Recognition Letters
A simultaneous learning framework for clustering and classification
Pattern Recognition
Training of support vector machines with Mahalanobis kernels
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Information Technology in Biomedicine
Fuzzy relational classifier trained by fuzzy clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A supervised clustering and classification algorithm for mining data with mixed variables
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
Computer Science Review
Ischemia detection with a self-organizing map supplemented by supervised learning
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A novel radial basis function neural network for discriminant analysis
IEEE Transactions on Neural Networks
Accuracy/Diversity and Ensemble MLP Classifier Design
IEEE Transactions on Neural Networks
Multiple view clustering using a weighted combination of exemplar-based mixture models
IEEE Transactions on Neural Networks
Classification of 3-D objects and faces employing view-based clusters
Computers and Electrical Engineering
Voting based extreme learning machine
Information Sciences: an International Journal
Multi-objective hybrid evolutionary algorithms for radial basis function neural network design
Knowledge-Based Systems
Simultaneous clustering and classification over cluster structure representation
Pattern Recognition
Information Sciences: an International Journal
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Traditional pattern recognition involves two tasks: clustering learning and classification learning. Clustering result can enhance the generalization ability of classification learning, while the class information can improve the accuracy of clustering learning. Hence, both learning methods can complement each other. To fuse the advantages of both learning methods together, many existing algorithms have been developed in a sequential fusing way by first optimizing the clustering criterion and then the classification criterion associated with the obtained clustering results. However, such kind of algorithms naturally fails to achieve the simultaneous optimality for two criteria, and thus has to sacrifice either the clustering performance or the classification performance. To overcome that problem, in this paper, we present a multiobjective simultaneous learning framework (MSCC) for both clustering and classification learning. MSCC utilizes multiple objective functions to formulate the clustering and classification problems, respectively, and more importantly, it employs the Bayesian theory to make these functions all only dependent on a set of the same parameters, i.e., clustering centers which play a role of the bridge connecting the clustering and classification learning. By simultaneously optimizing the clustering centers embedded in these functions, not only the effective clustering performance but also the promising classification performance can be simultaneously attained. Furthermore, from the multiple Pareto-optimality solutions obtained in MSCC, we can get an interesting observation that there is complementarity to great extent between clustering and classification learning processes. Empirical results on both synthetic and real data sets demonstrate the effectiveness and potential of MSCC.