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.
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)
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.
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.
1057171
ZPL's WYSIWYG performance model
ZPL is a parallel array language designed for high performance scientific and engineering computations. Unlike other parallel languages, ZPL is founded on a machine model (the CTA) that accurately abstracts contemporary MIMD parallel computers. This makes it possible to correlate ZPL programs with machine behavior. As a result, programmers can reason about how code will perform on a typical parallel machine and thereby make informed decisions between alternative programming solutions. The paper describes ZPL's performance model and its syntactic cues for conveying operation cost. The what you see is what you get (WYSIWYG) nature of ZPL operations is demonstrated on the IBM SP-2, Intel Paragon, SGI Power Challenge, and Cray T3E. Additionally, the model is used to evaluate two algorithms for matrix multiplication. Experiments show that the performance model correctly predicts the faster solution on all four platforms for a range of problem sizes.
1207322
Dynamic integrated scheduling of hard real-time, soft real-time, and non-real-time processes
Real-time systems are growing in complexity and real-time and soft real-time applications are becoming common in general-purpose computing environments. Thus, there is a growing need for scheduling solutions that simultaneously support processes with a variety of different timeliness constraints. Toward this goal we have developed the resource allocation/dispatching (RAD) integrated scheduling model and the rate-based earliest deadline (RBED) integrated multi-class real-time scheduler based on this model. We present RAD and the RBED scheduler and formally prove the correctness of the operations that RBED employs. We then describe our implementation of RBED and present results demonstrating how RBED simultaneously and seamlessly supports hard real-time, soft real-time, and best-effort processes.
5592923
Temperature control with a neural fuzzy inference network
Although multilayered backpropagation neural networks (BPNNs) have demonstrated high potential in adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained online to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is compared to that of the traditional PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared, with respect to set point regulation, ramp-point tracking, and the influence of unknown impulse noise and large parameter variation in the temperature control system. The proposed NFIN scheme has the best control performance.
322920
Introduction to the special issue on statistical language modeling
The goal of statistical language modeling (SLM) is to estimate the likelihood (or probability) of a word string. SLM is fundamental to many natural language applications like automatic speech recognition (ASR) [Jelinek 1990], statistical machine translation (SMT) [Brown et al. 1993], and Asian language text input [Gao et al. 2002a]. The research on SLM basically involves two main tasks: modeling and estimation. Modeling is to determine the structure of a statistical model; estimation is to determine the free parameters of the model using training data. SLM usually uses a parametric model with Maximum Likelihood Estimation (MLE) and various smoothing methods to tackle data sparseness problems. Different statistical models have been proposed in the past, but n-gram models (in particular, bigram and trigram models) still dominate SLM research. SLM has recently been demonstrated as an effective framework for a few new applications, such as question answering [Berger 2001], text summarization, paraphrasing [Barzilay and Lee 2004], and information retrieval [Croft and Lafferty 2003]. However, these new applications come with new challenges. For example, in the SLM approaches to information retrieval, a language model has to be trained on a single document, an extremely small training set; while in ASR, a language model is typically trained on a million word corpus. The recent development of related techniques stimulates new modeling and estimation methods that are beyond the scope of the traditional approaches. Two representative examples of such techniques are statistical parsing and discriminative training. With the ever-increasing popularity of SLM, we think that it is the right time to assemble a special issue reflecting recent advances in both its theory and applications. It __________________________________________________________________________________________
3052468
Performance Modeling and Analysis for Resource Scheduling in Data Grids
Data Grids normally deal with large data-intensive problems on geographically distributed resources; yet, most current research on performance evaluation of resource scheduling in Data Grids is based on simulation techniques, which can only consider a limited range of scenarios. In this paper, we propose a formal framework via Stochastic Petri Nets to deal with this problem. Within this framework, we model and analyze the performance of resource scheduling in Data Grids, allowing for a wide variety of job and data scheduling algorithms. As a result of our research, we can investigate more scenarios with multiple input parameters. Moreover, we can evaluate the combined effectiveness of job and data scheduling algorithms, rather than study them separately.