22088959
Analysis of Nonstationary Time Series Using Support Vector Machines
Time series from alternating dynamics have many important applications. In [5], the authors propose an approach to solve the drifting dynamics. Their method directly solves a non-convex optimization problem. In this paper, we propose a strategy which solves a sequence of convex optimization problems by using modified support vector regression. Experimental results showing its practical viability are presented and we also discuss the advantages and disadvantages of the proposed approach.
7664224
Probability Estimates for Multi-class Classification by Pairwise Coupling
Pairwise coupling is a popular multi-class classification method that combines all comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement. We show conceptually and experimentally that the proposed approaches are more stable than the two existing popular methods: voting and the method by Hastie and Tibshirani (1998)
3084140
Ranking individuals by group comparisons
This paper proposes new approaches to rank individuals from their group competition results. Many real-world problems are of this type. For example, ranking players from team games is important in some sports. We propose an exponential model to solve such problems. To estimate individual rankings through the proposed model we introduce two convex minimization formulas with easy and efficient solution procedures. Experiments on real bridge records and multi-class classification demonstrate the viability of the proposed model.
36777484
A Simple Decomposition Method for Support Vector Machines
The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for bound-constrained SVM formulations we demonstrate that the working set selection is not a trivial task. Then from the experimental analysis we propose a simple selection of the working set which leads to faster convergences for difficult cases. Numerical experiments on different types of problems are conducted to demonstrate the viability of the proposed method.
20106221
Radius Margin Bounds for Support Vector Machines with the RBF Kernel
An important approach for efficient support vector machine (SVM) model selection is to use differentiable bounds of the leave-one-out (loo) error. Past efforts focused on finding tight bounds of loo (e.g., radius margin bounds, span bounds). However, their practical viability is still not very satisfactory. Duan, Keerthi, and Poo (2003) showed that radius margin bound gives good prediction for L2-SVM, one of the cases we look at. In this letter, through analyses about why this bound performs well for L2-SVM, we show that finding a bound whose minima are in a region with small loo values may be more important than its tightness. Based on this principle, we propose modified radius margin bounds for L1-SVM (the other case) where the original bound is applicable only to the hard-margin case. Our modification for L1-SVM achieves comparable performance to L2-SVM. To study whether L1-or L2-SVM should be used, we analyze other properties, such as their differentiability, number of support vectors, and number of free support vectors. In this aspect, L1-SVM possesses the advantage of having fewer support vectors. Their implementations are also different, so we discuss related issues in detail.
6373317
QoS-aware Composite Service Selection in Grids
Grid computing powered by Web service technology is creating abundant service-oriented applications which are promoting the sharing and collaboration of global resources. Complex tasks can be fulfilled quickly and efficiently by combining (physically distributed) individual services into a competent composite service. Since a lot of available individual services have the same functional properties while different non-functional properties (e.g. quality of service), decisions have to be made to select superior individual services from candidates for service composition. In this paper, the authors present a quality of service (QoS) model of service composition, as well as a composite service selection approach which can optimally choose the competent services, in the sense of satisfying users QoS requirement, for the demanded composite services with specified composite structures
18939289
SARI: self-authentication-and-recovery image watermarking system
In this project, we designed a novel image authentication system based on a our semi-fragile watermarking technique. The system, called SARI, can accept quantization-based lossy compression to a determined degree without any false alarm and can sensitively detect and locate malicious manipulations. It's the first system that has such capability in distinguishing malicious attacks from acceptable operations. Furthermore, the corrupted area can be approximately recovered by the information hidden in the other part of the contentimage. The amount of information embedded in our SARI system has nearly reached the theoretical maximum zero-error information hiding capacity of digital images. The software prototype includes two parts - the watermark embedder that's freely distributed and the authenticator that can be deployed online as a third-party service or used in the recipient side.
15301928
New techniques on deformed image motion estimation and compensation
In this paper, new techniques for deformed image motion estimation and compensation using variable-size block-matching are proposed, which can be applied to an image sequence compression system or a moving object recognition system. The motion estimation and compensation techniques have been successfully applied in the area of image sequence coding. Many research papers on improving the performance of these techniques have been published; many directions are proposed, which can all lead to better performance than the conventional techniques. Among them, both generalized block-matching and variable-size block-matching are successfully applied in reducing the data rate of compensation error and motion information, respectively. These two algorithms have their merits, but suffer from their drawbacks. Moreover, reducing the data rate in compensation error is sometimes increasing the data rate in motion information, or vice versa. Based on these two algorithms, we propose and examine several algorithms which are effective in reducing the data rate. We then incorporate these algorithms into a system, in which they work together to overcome the disadvantages to individual and keep their merits at the same time. The proposed system can optimally balance the amount of data rate in two aspects (i.e., compensation error and motion information). Experimental results show that the proposed system outweighs the conventional techniques. Since we propose a recovery operation which tries to recover the incorrect motion vectors from the global motion, this proposed system can also be applied to the moving object recognition in image sequences.
5221251
On Stochastic High-Level Petri Nets
5812336
A Key Embedded Video Codec for Secure Video Multicast
The problem of controlling access to multimedia multicasts requires the distribution and maintenance of keying information. This paper proposes a new key embedded video codec for the media-dependent approach that involves the use of a data-dependent channel, and can be achieved for multimedia by using data embedding techniques. Using data embedding to convey keying information can provide an additional security and reduce the demand of bandwidth. In this paper, we develop a new statistical data embedding algorithm on compression-domain, then, by combining FEC (Forward Error Correction) algorithm and our proposed key information frame format. After a series of simulation experiments, we find that the new codec can satisfy the special demand of media-dependent approach. At the same time, the codec provides good performance.