474823
Boosting the Feature Space: Text Classification for Unstructured Data on the Web
The issue of seeking efficient and effective methods for classifying unstructured text in large document corpora has received much attention in recent years. Traditional document representation like bag-of-words encodes documents as feature vectors, which usually leads to sparse feature spaces with large dimensionality, thus making it hard to achieve high classification accuracies. This paper addresses the problem of classifying unstructured documents on the Web. A classification approach is proposed that utilizes traditional feature reduction techniques along with a collaborative filtering method for augmenting document feature spaces. The method produces feature spaces with an order of magnitude less features compared with a baseline bag-of-words feature selection method. Experiments on both real-world data and benchmark corpus indicate that our approach improves classification accuracy over the traditional methods for both support vector machines and AdaBoost classifiers.
8249361
SUDEPHIC: Self-Tuning Density-Based Partitioning and Hierarchical Clustering
Clustering is one of the primary techniques in data mining, for which to find the user expecting result is a major issue. However, to dynamically specify the parameters for clustering algorithms presents an obstacle for users. This paper firstly introduces a novel density-based partitioning and hierarchical algorithm, which makes it easy to employ synthetic feedback mechanism in clustering. Additionally, by investigating into the relation between parameters and the clustering result, we propose a self-tuning technique for the setting of parameters. Meanwhile, the density distribution within a cluster can be expressed in the result for the user to specify the cluster’s feature. The algorithm is both evaluated in theory and practice. It outperforms many existing algorithms both in efficiency and quality.
13242297
A clustering method for web data with multi-type interrelated components
Traditional clustering algorithms work on "flat" data, making the assumption that the data instances can only be represented by a set of homogeneous and uniform features. Many real world data, however, is heterogeneous in nature, comprising of multiple types of interrelated components. We present a clustering algorithm, K-SVMeans, that integrates the well known K-Means clustering with the highly popular Support Vector Machines(SVM) in order to utilize the richness of data. Our experimental results on authorship analysis of scientific publications show that K-SVMeans achieves better clustering performance than homogeneous data clustering.
12261862
Capacity of reproducing kernel spaces in learning theory
The capacity of reproducing kernel Hilbert spaces (RKHS) plays an essential role in the analysis of learning theory. Covering numbers and packing numbers of balls of these reproducing kernel spaces are important measurements of this capacity. We first present lower bound estimates for the packing numbers by means of nodal functions. Then we show that if a Mercer kernel is C/sup s/ (for some s>0 being not an even integer), the RKHS associated with this kernel can be embedded into C/sup s/2/. This gives upper-bound estimates for the covering number concerning Sobolev smooth kernels.Examples and applications to V/sub /spl gamma// dimension and Tikhonov (1977) regularization are presented to illustrate the upper- and lower-bound estimates.
20468093
A Multiplier Structure Based on a Novel Real-time CSD Recoding
Implementation of digital signal processing (DSP) algorithms in hardware, such as field programmable gate arrays (FPGAs), requires a large number of multiplications. In this paper, we introduce a novel multiplier structure that converts from 2's complement to canonical signed digit (CSD) representation in real time. The proposed algorithm increases the number of zero partial products (which can be simplified by shift operations) to approximately 66.7% compared with 50% for modified Booth's recoding. Also, the proposed hardware structure reduces the non-zero partial products to a minimum, and consequently the number of arithmetic operations in the carry-save structure is reduced. So, our proposed hardware decreases both the time required for multiplication and the power consumption of the multiplier. Furthermore, because the proposed structure uses real time CSD recoding, and does not require a fixed value for the multiplier input to be known a priori, the proposed multiplier can be used to implement digital filters with non-fixed filter coefficients, such as adaptive filters.
206963206
Efficient adaptive Volterra filters for active nonlinear noise control with a linear secondary-path
Using a nonlinear filter to control a linear device has been studied in Strauch and Mulgrew (1998), and been proved to be effective in active noise control (ANC) when: i) the secondary path has a linear and nonminimum phase transfer function, and the reference noise is a nonlinear, predictable noise or non-Gaussian noise; ii) the primary path has a nonlinear effect. Tan et al. (2001) reinforced these statements and successfully implemented this idea with an adaptive Volterra filter, and developed an algorithm called the Volterra filtered-x LMS (VFXLMS) algorithm. However this VFXLMS suffers from a heavy computational burden as well as stability problems. In this paper, we provide several alternatives to VFXLMS: the modified Volterra LMS algorithm that increases the convergence rate by slightly increasing the computational burden, the filtered-error Volterra LMS algorithms (including the adjoint Volterra LMS algorithm and the secondary-path equalization Volterra LMS algorithm) that greatly reduce the computational complexity and improves the convergence rate. Analysis and simulation results prove the effectiveness of our proposed adaptive algorithms.