A novel method for measuring semantic similarity for XML schema matching
Expert Systems with Applications: An International Journal
Prediction of pricing and hedging errors for equity linked warrants with Gaussian process models
Expert Systems with Applications: An International Journal
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Expert Systems with Applications: An International Journal
An iterative semi-explicit rating method for building collaborative recommender systems
Expert Systems with Applications: An International Journal
Kernel-based Monte Carlo simulation for American option pricing
Expert Systems with Applications: An International Journal
User credit-based collaborative filtering
Expert Systems with Applications: An International Journal
Constructing sparse kernel machines using attractors
IEEE Transactions on Neural Networks
Improving memory-based collaborative filtering via similarity updating and prediction modulation
Information Sciences: an International Journal
Fast support-based clustering method for large-scale problems
Pattern Recognition
Predicting a distribution of implied volatilities for option pricing
Expert Systems with Applications: An International Journal
Dynamic pattern denoising method using multi-basin system with kernels
Pattern Recognition
Expert Systems with Applications: An International Journal
Forecasting trends of high-frequency KOSPI200 index data using learning classifiers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Uniformly subsampled ensemble (USE) for churn management: Theory and implementation
Expert Systems with Applications: An International Journal
Forecasting nonnegative option price distributions using Bayesian kernel methods
Expert Systems with Applications: An International Journal
Sequential manifold learning for efficient churn prediction
Expert Systems with Applications: An International Journal
Position regularized Support Vector Domain Description
Pattern Recognition
Efficient web data classification techniques using semi-supervise learning algorithm
Proceedings of the 5th ACM COMPUTE Conference: Intelligent & scalable system technologies
Probabilistic generative ranking method based on multi-support vector domain description
Information Sciences: an International Journal
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A novel learning algorithm for semisupervised classification is proposed. The proposed method first constructs a support function that estimates a support of a data distribution using both labeled and unlabeled data. Then, it partitions a whole data space into a small number of disjoint regions with the aid of a dynamical system. Finally, it labels the decomposed regions utilizing the labeled data and the cluster structure described by the constructed support function. Simulation results show the effectiveness of the proposed method to label out-of-sample unlabeled test data as well as in-sample unlabeled data