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.
41845464
Advances in Natural Computation, First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I
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.
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.
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.
64402095
Trends in Neural Computation (Studies in Computational Intelligence)
1213875
A simplified LVI-based primal-dual neural network for repetitive motion planning of PA10 robot manipulator starting from different initial states
This paper presents a simplified primal-dual neural network based on linear variational inequalities (LVI) for online repetitive motion planning of PA10 robot manipulator. To do this, a drift-free criterion is exploited in the form of a quadratic function. In addition, the repetitive-motion-planning scheme could incorporate the joint limits and joint velocity limits simultaneously. Such a scheme is finally reformulated as a time-varying quadratic program (QP). As a QP real-time solver, the simplified LVI-based primal-dual neural network (LVI-PDNN) is designed based on the QP-LVI conversion and Karush-Kuhn-Tucker (KKT) conditions. It has a simple piecewise-linear dynamics and could globally exponentially converge to the optimal solution of strictly-convex quadratic-programs. The simplified LVI-PDNN model is simulated based on PA10 robot arm, and simulation results show the effective remedy of the joint angle drift problem of PA10 robot.
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.
15163337
A Database Encryption Scheme for Enhanced Security and Easy Sharing
Database encryption is a crucial technique in the security mechanisms of database. It is widely recognized as one of the key issues of data security. Current techniques of sharing the keys and the encrypted data for databases are neither convenient nor flexible in the real applications. Inspired by the PGP technique, we propose a novel database encryption scheme for enhanced data sharing inside a database, while preserving data privacy. It is characterized by both the fast speed of the conventional encryption and the convenience of key distribution of public key encryption. It also provides secured storage for security related data and effective key management, which enables the encrypted data to be shared conveniently. The scheme has been implemented and successfully applied on Oscar, a commercial DBMS developed by us
18102767
Resilient Rights Protection for Product Data
With the development of advance manufacture applications, enterprise must interact with their suppliers and customers to share more product data information. Consequently, copyright and integrity protection over product data is becoming an urgent requirement. Product data information consists of two parts: product structure and part content, besides protecting the three-dimensional (3D) model of part by watermarking, we also need to protect product structure information, which describes the relations among product parts and can be represented as a tree. In this paper, based on the 3D polygonal meshes watermark technology, the structure/semi-structure data watermark technologies and the idea of Fractal, we propose a novel watermark scheme for tree-structure product data with the value lying both in product structure and in part node content, which gives a more comprehensive and resilient right protection for both part node content and product structure. Moreover it is particularly effective when resisting invertibility attacks
38388542
On a Class of Preconditioning Methods for Dense Linear Systems from Boundary Elements
In this paper we discuss several sparse-matrix-based preconditioners suitable for preconditioning dense linear systems of boundary element equations. All preconditioners involve only O(n) nonzeros. We provide a framework for constructing operator-splitting-based preconditioners and use it to analyze a class of sparse preconditioners. For singular integral equations, a more efficient preconditioner is proposed that has a band-2 structure.