4925614
Combining Competitive Learning Networks of Various Representations for Sequential Data Clustering
A process for the preparation of oil-soluble Mannich condensation products wherein an amine is prereacted with a sulfonic acid or ammonium sulfonate, and the prereaction product is reacted with an aldehyde and an oxidized olefin polymer.
15381679
A Modular Neural Network Architecture for Pattern Classification Based on Different Feature Sets
We propose a novel connectionist method for the use of different feature sets in pattern classification. Unlike traditional methods, e.g., combination of multiple classifiers and use of a composite feature set, our method copes with the problem based on an idea of soft competition on different feature sets developed in our earlier work. An alternative modular neural network architecture is proposed to provide a more effective implementation of soft competition on different feature sets. The proposed architecture is interpreted as a generalized finite mixture model and, therefore, parameter estimation is treated as a maximum likelihood problem. An EM algorithm is derived for parameter estimation and, moreover, a model selection method is proposed to fit the proposed architecture to a specific problem. Comparative results are presented for the real world problem of speaker identification.
6083596
Improved learning algorithms for mixture of experts in multiclass classification
Mixture of experts (ME) is a modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME architecture for adjusting the parameters and the iteratively reweighted least squares (IRLS) algorithm is used to perform maximization in the inner loop [Jordan, M.I., Jacobs, R.A. (1994). Hierarchical mixture of experts and the EM algorithm, Neural Computation, 6(2), 181-214]. However, it is reported in literature that the IRLS algorithm is of instability and the ME architecture trained by the EM algorithm, where IRLS algorithm is used in the inner loop, often produces the poor performance in multiclass classification. In this paper, the reason of this instability is explored. We find out that due to an implicitly imposed incorrect assumption on parameter independence in multiclass classification, an incomplete Hessian matrix is used in that IRLS algorithm. Based on this finding, we apply the Newton-Raphson method to the inner loop of the EM algorithm in the case of multiclass classification, where the exact Hessian matrix is adopted. To tackle the expensive computation of the Hessian matrix and its inverse, we propose an approximation to the Newton-Raphson algorithm based on a so-called generalized Bernoulli density. The Newton-Raphson algorithm and its approximation have been applied to synthetic data, benchmark, and real-world multiclass classification tasks. For comparison, the IRLS algorithm and a quasi-Newton algorithm called BFGS have also been applied to the same tasks. Simulation results have shown that the use of the proposed learning algorithms avoids the instability problem and makes the ME architecture produce good performance in multiclass classification. In particular, our approximation algorithm leads to fast learning. In addition, the limitation of our approximation algorithm is also empirically investigated in this paper.
34883067
Personalized News Reading via Hybrid Learning
In this paper, we present a personalized news reading prototype where latest news articles published by various on-line news providers are automatically collected, categorized and ranked in light of a user’s habits or interests. Moreover, our system can adapt itself towards a better performance. In order to develop such an adaptive system, we proposed a hybrid learning strategy; supervised learning is used to create an initial system configuration based on user’s feedbacks during registration, while an unsupervised learning scheme gradually updates the configuration by tracing the user’s behaviors as the system is being used. Simulation results demonstrate satisfactory performance.
41845464
Advances in Natural Computation, First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I
15908834
Ensemble Learning with Active Data Selection for Semi-Supervised Pattern Classification
Unlike traditional pattern classification, semi-supervised learning provides a novel technique to make use of both labeled and unlabeled data for improving the performance of classification. In general, there are two critical issues for semi-supervised learning of discriminative classifiers; i.e., how to create an initial classifier of a good generalization capability with the limited labeled data and the how to make an effective use of unlabeled data without degradation of the established classifier. To tackle two aforementioned problems, we propose an ensemble learning approach based on a recent active data selection strategy, where ensemble learning would yield good generalization and active data selection tends to choose the unlabeled data more likely resulting in an improvement during semi-supervised learning. By using an ensemble of K-NN classifiers, we demonstrate the effectiveness of our approach on a synthetic data classification and a facial expression recognition tasks.
5841267
The Data Mining Technology Based on CIMS and its Application on Automotive Remanufacturing
Nowadays, with the development of computer technology, data mining has been widely used in various fields. This paper describes a CIMSMINER that combines the data mining with CIMS (computer integrated manufacturing system) and instructs its objectives, model, physical architecture and methods. Considering the characteristics of remanufacturing of automotive products in China, the CIMSMINER is used to get the information concourse together and obtain the data mining results to help the improvement of products. The application in automotive remanufacturing is a reform to make the automotive product information chain be not only an information carrier, but also an information miner. Currently, the government strongly emphasizes energy saving and emission reducing, which is closely related to the sustainable development of China. Obviously, CIMSMINER is an effective tool to support the implementation of this policy.
5352108
Efficient parallelization of the iterative solution of a coupled fluid-structure interaction problem
In this paper we consider the parallelization of the generation and iterative solution of coupled linear systems modelling the interaction of an acoustic field in a fluid medium with an elastic structure immersed in the fluid. The particular case studied is that of a hollow steel sphere in water. The aim of the work is to speed up the generation and solution of the systems. We describe the methods used, which involve special sparse storage arrangements and a novel application of a sparse approximate inverse preconditioning technique, and present results showing that the methods are very effective in terms of speeding up the generation and iterative solution of the systems. Copyright © 2007 John Wiley & Sons, Ltd.
10775107
An Architecture Approach to Dynamic Policy in RBAC
Dynamic policy enables the system to adjust the policies according to the changing circumstance, and makes the system more flexible and adaptive. We have proposed a dynamic model and the idea is to dynamically change the policy according to a pair of states rather than one state, which provides more information for the policy decision making, thus makes policy more accurate. The dynamic policy architecture based on this model is built in this paper, and we describe the components and steps of detecting a change, deciding the pair of current and previous states and picking up the policy according to the change.
14448585
Application research of an LFB management and development model in ForCES router
The router based on ForCES (Forwarding and Control Element Separation) meets the open, flexible and programmable demands of the next generation networks. It is one important trend of the next generation routers. Our research team is key participants in the ForCES work and authors of the IETF ForCES protocol. A LFB management and development model based on ForCES router software architecture was illustrated. The ForCES router software structure was first discussed. Based on that, the model was presented. Based on that, the architecture of this model was described. The key technologies on how to manage resource library were described, including the method of LFB resource library management and the mechanism of ForCES LFB resource internal storage. In order to testify the feasibility, an implementation of the model was presented, and an example of an IPv6 forwarding service implementation was given.
24158595
A Fractal Watermark Solution for Product Data
Copyright and integrity protection over product data is becoming an urgent requirement, because in a Distributed Networked Manufacturing environment, enterprises have to interact with each other to share product data. In this paper, we introduce a novel watermarking solution for product data based on the idea of fractal, which classifies product data watermarks into three tiers: fractal watermark, tree-structure watermark, and tree-node watermark. It embeds watermarks not only in value-carrying nodes but also in product structures that "glue" all the nodes together by embedding multiple structure watermarks in product data. Simulation results show that the proposed technique is robust against various forms of malicious attacks of product data, and naturally immune from structure transformation attacks and subtractive (sub-tree) attacks. Moreover, the solution can protect tree-structure data whose knowledge is mainly embodied in the tree structure efficiently. It is very easy to implement and popularize.