17327326
UBM-based incremental speaker adaptation
This paper addresses a novel algorithm of incremental speaker adaptation (ISA) based on universal background model (UBM) for saving storage and real-time processing. This algorithm can be seen as an extension of traditional speaker adaptation. It consists of two steps, adaptation and combination. It not only considers the speaker's characteristics in limited training data, but also prohibits over-fitting of the updated model. The incremental adaptation algorithm needs little storage and meets the requirement of real-time processing. In order to evaluate the efficiency and effectivity of the proposed approach, a real-time speaker segmentation system for broadcasting news is built. Experiment results demonstrate that our approach yields real time operation and achieves satisfactory performance.
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.
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
1262399
Efficient Holistic Twig Join Algorithm on XML Documents with Optimization Rules and Index
Efficiently finding all occurrences of a twig pattern is a core operation of XML query processing. In this paper, we first propose a generic twig join algorithm TJFGeneric which only scans the leaf query nodes using a cursor interface to return elements. And then we present TJFGeneric+ algorithm incorporating two optimization rules into TJFGeneric to explore potential benefit of disk read and CPU cost. In particular, available indices (such as B+-tree) are applied to skip the elements that do not participate in final results. Experimental results on various datasets indicate that our algorithms outperform previous approaches significantly.
6117831
MATLAB Simulink modeling and simulation of LVI-based primal-dual neural network for solving linear and quadratic programs
In view of parallel-processing nature and circuit-implementation convenience, recurrent neural networks are often employed to solve optimization problems. Recently, a primal-dual neural network based on linear variational inequalities (LVI) was developed by Zhang et al. for the online solution of linear-programming (LP) and quadratic-programming (QP) problems simultaneously subject to equality, inequality and bound constraints. For the final purpose of field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) realization, we investigate in this paper the MATLAB Simulink modeling and simulative verification of such an LVI-based primal-dual neural network (LVI-PDNN). By using click-and-drag mouse operations in MATLAB Simulink environment, we could quickly model and simulate complicated dynamic systems. Modeling and simulative results substantiate the theoretical analysis and efficacy of the LVI-PDNN for solving online the linear and quadratic programs.
16722943
The Euclidean Orienteering Problem Revisited
We consider the rooted orienteering problem: Given a set $P$ of $n$ points in the plane, a starting point $r \in P$, and a length constraint $B$, one needs to find a path starting from $r$ that visits as many points of $P$ as possible and of length not exceeding $B$. We present a $(1-\varepsilon)$-approximation algorithm for this problem that runs in $n^{O(1/\varepsilon)}$ time; the computed path visits at least $ (1-\varepsilon)k_{\mathrm{opt}}$ points of $P$, where $k_{\mathrm{opt}}$ is the number of points visited by an optimal solution. This is the first polynomial time approximation scheme for this problem. The algorithm also works in higher dimensions.
17533153
OTJFast: Processing Ordered XML Twig Join Fast
Thanks to standard specifications for Web services, more and more data is represented and exchanged in XML. Efficiently finding all occurrences of a twig pattern is a core operation of XML query processing. Recently, a large number of holistic twig join algorithms have been proposed, however ordered XML twig join has been ignored in most of them. In this paper we design a novel algorithm called OTJFast to process ordered XML twig joins efficiently which only need to access the elements of the leaf query nodes. Experimental results show the benefits of our algorithm in term of the size of intermediate results, the number of element reads and the execution time.
33477464
Boundary-Based Feature Extraction and Recognition of Breast Tumors Using Support Vector Machine