A Novel Fuzzy Neural Network with Fast Training and Accurate Generalization
For the reason of all parameters in a conventional fuzzy neural network (FNN) needed to be adjusted iteratively, learning can be very slow and may suffer from local minima. To overcome these problems, we propose a novel FNN in this paper, which shows a fast speed and accurate generalization. First we state the universal approximation theorem for an FNN with random membership function parameters (FNN-RM). Since all the membership function parameters are arbitrarily chosen, the proposed FNN-RM algorithm needs to adjust only the output weights of FNNs. Experimental results on function approximation and classification problems show that the new algorithm not only provides thousands of times of speed-up over traditional learning algorithms, but also produces better generalization performance in comparison to other FNNs.

title: Classification using support vector machines with graded resolution
abstract: A method which we call support vector machine with graded resolution (SVM-GR) is proposed in this paper. During the training of the SVM-GR, we first form data granules to train the SVM-GR and remove those data granules that are not support vectors. We then use the remaining training samples to train the SVM-GR. Compared with the traditional SVM, our SVM-GR algorithm requires fewer training samples and support vectors, hence the computational time and memory requirements for the SVM-GR are much smaller than those of a conventional SVM that use the entire dataset. Experiments on benchmark data sets show that the generalization performance of the SVM-GR is comparable to the traditional SVM.

title: Binary Tree Support Vector Machine Based on Kernel Fisher Discriminant for Multi-classification
abstract: In order to improve the accuracy of the conventional algorithms for multi-classifications, we propose a binary tree support vector machine based on Kernel Fisher Discriminant in this paper. To examine the training accuracy and the generalization performance of the proposed algorithm, One-against-All, One-against-One and the proposed algorithms are applied to five UCI data sets. The experimental results show that in general, the training and the testing accuracy of the proposed algorithm is the best one, and there exist no unclassifiable regions in the proposed algorithm.

title: Learning to Identify Unexpected Instances in the Test Set
abstract: Traditional classification involves building a classifier using labeled training examples from a set of predefined classes and then applying the classifier to classify test instances into the same set of classes. In practice, this paradigm can be problematic because the test data may contain instances that do not belong to any of the previously defined classes. Detecting such unexpected instances in the test set is an important issue in practice. The problem can be formulated as learning from positive and unlabeled examples (PU learning). However, current PU learning algorithms require a large proportion of negative instances in the unlabeled set to be effective. This paper proposes a novel technique to solve this problem in the text classification domain. The technique first generates a single artificial negative document AN. The sets P and {AN} are then used to build a naive Bayesian classifier. Our experiment results show that this method is significantly better than existing techniques.

title: An New Global Dynamic Scheduling Algorithm with Multi-Hop Path Splitting and Multi-Pathing Using GridFTP

title: A Scalable Peer-to-Peer Overlay for Applications with Time Constraints
abstract: With the development of Internet, p2p is increasingly receiving attention in research. Recently, a class of p2p applications with time constraints appear. These applications require a short time to locate the resource and(or) a low transit delay between the resource user and the resource holder, such as Skype, MSN. In this paper we propose a scalable p2p overlay for applications with time constraints. Our system provides supports for just two operations for uplayered p2p applications: (1) Given a resource key and the node's IP who holds the resource, it registers the resource information to the associated node in at most two overlay hops; and (2) Given a resource key and a time constraint(0 for no constraint), it returns if possible a path(one or two overlay hops) to the resource holder, and the transit delay of the path is lower than the time constraint. Results from theoretical analysis and simulations show that our system is viable and scalable.

title: A Population-Based Incremental Learning Algorithm with Elitist Strategy
abstract: The population-based incremental learning (PBIL) is a novel evolutionary algorithm combined the mechanisms of the Genetic Algorithm with competitive learning. In this paper, the influence of the number of selected best solutions on the convergence speed of the PBIL is studied by experiment. Based on experimental results, a PBIL algorithm with elitist strategy, named Double Learning PBIL (DLPBIL), is proposed. The new algorithm learns both the selected best solutions in current population and the optimal solution found so far in the algorithm at same time. Experimental results show that the DLPBIL out-performs the standard PBIL. Both the convergence speed and the solution quality are improved.