11225382
Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width . This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.
41121683
Decomposition Methods for Linear Support Vector Machines
In this letter, we show that decomposition methods with alpha seeding are extremely useful for solving a sequence of linear support vector machines (SVMs) with more data than attributes. This strategy is motivated by Keerthi and Lin (2003), who proved that for an SVM with data not linearly separable, after C is large enough, the dual solutions have the same free and bounded components. We explain why a direct use of decomposition methods for linear SVMs is sometimes very slow and then analyze why alpha seeding is much more effective for linear than nonlinear SVMs. We also conduct comparisons with other methods that are efficient for linear SVMs and demonstrate the effectiveness of alpha seeding techniques in model selection.
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.
13394593
Generalized Bradley-Terry Models and Multi-Class Probability Estimates
The Bradley-Terry model for obtaining individual skill from paired comparisons has been popular in many areas. In machine learning, this model is related to multi-class probability estimates by coupling all pairwise classification results. Error correcting output codes (ECOC) are a general framework to decompose a multi-class problem to several binary problems. To obtain probability estimates under this framework, this paper introduces a generalized Bradley-Terry model in which paired individual comparisons are extended to paired team comparisons. We propose a simple algorithm with convergence proofs to solve the model and obtain individual skill. Experiments on synthetic and re al data demonstrate that the algorithm is useful for obtaining multi-class probability estimates. Moreover, we discuss four extensions of the proposed model: 1) weighted individual skill, 2) home-field advantage, 3) ties, and 4) comparisons with more than two teams.
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.
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.
338397
Design a congestion controller based on sliding mode variable structure control
As an effective mechanism acting on the intermediate nodes to support end-to-end congestion control, Active Queue Management (AQM) takes a trade-off between link utilization and delay experienced by data packets. Most existing AQM algorithms are heuristic, and lack a systematic and theoretical design and analysis approach. From the viewpoint of control theory, the AQM system can be regarded as a typical regulating system. Although the PI controller for AQM outperforms the RED algorithm, the mismatches in a simplified TCP flow model inevitably degrade the performance of controller designed using classical control theory. In this paper, a robust controller for AQM is developed based on Sliding Mode Variable Structure Control (SMVS). Because it is insensitive to noise and variance of parameters, it is very suitable for time-varying network systems. The principles and design of the SMVS controller are presented in detail. The integrated performance is evaluated using ns simulations. The results show that the SMVS is responsive and robust against disturbance. At the same time, a complete comparison between SMVS controller and PI controller is made. The conclusion is that both transient and steady performance of the SMVS controller is superior to those of PI controller, thus the SMVS controller can readily achieve to the AQM objectives.
30069596
Survivability Analysis of Grid Resource Management System Topology
The resource management system is one of the essential components of Grids. A resource management system may be made up of one or many resource management nodes. The organization manner of the resource management nodes, i.e. the topology of a resource management system, is a key factor that should be considered carefully during the architecture design of resource management systems with respect to some specified requirements (e.g., performance, survivability). In this paper, a new survivability definition is proposed and the survivability of three typical Grid resource management system topologies (centralized, hierarchical, and peer-to-peer) is analyzed.
9978156
Comparing frameworks and layered refinement
Object-oriented frameworks are a popular mechanism for building and evolving large applications and software product lines. We describe an alternative approach to software construction, Java Layers (JL), and evaluate JL and frameworks in terms of flexibility, ease of use, and support for evolution. Our experiment compares Schmidt's (1998) ACE framework against a set of ACE design patterns that have been implemented in JL. We show how problems of framework evolution and overfeaturing can be avoided using JL's component model, and we demonstrate that JL scales better than frameworks as the number of possible application features increases. Finally, we describe how constrained parametric polymorphism and a small number of language features can support JL's model of loosely coupled components and stepwise program refinement.
14630306
Fuzzy neural network classification design using support vector machine
Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network classification (SVFNNC) is proposed. The SVFNNC combines the superior classification power of support vector machine (SVM) in high reasoning of FNN in handling uncertainty information. The learning algorithm consists of two learning phases. In the phase 1, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the phase 2, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. To investigate the effectiveness of the proposed SVFNNC, it is applied to the iris, vehicle and dna datasets. Experimental results show that the proposed SVFNNC can achieve good classification performance with drastically reduced number of fuzzy kernel functions.
27000155
Towards a Machine-Independent Solution of Sparse Cholesky Factorization