7659070
Statistical Properties and Adaptive Tuning of Support Vector Machines
In this paper we consider the statistical aspects of support vector machines (SVMs) in the classification context, and describe an approach to adaptively tuning the smoothing parameter(s) in the SVMs. The relation between the Bayes rule of classification and the SVMs is discussed, shedding light on why the SVMs work well. This relation also reveals that the misclassification rate of the SVMs is closely related to the generalized comparative Kullback-Leibler distance (GCKL) proposed in Wahba (1999, Scholkopf, Burges, & Smola (Eds.), Advances in Kernel Methods—Support Vector Learning. Cambridge, MA: MIT Press). The adaptive tuning is based on the generalized approximate cross validation (GACV), which is an easily computable proxy of the GCKL. The results are generalized to the unbalanced case where the fraction of members of the classes in the training set is different than that in the general population, and the costs of misclassification for the two kinds of errors are different. The main results in this paper have been obtained in several places elsewhere. Here we take the opportunity to organize them in one place and note how they fit together and reinforce one another. Mostly the work of the authors is reviewed.
5801524
Support Vector Machines for Classification in Nonstandard Situations
The majority of classification algorithms are developed for the standard situation in which it is assumed that the examples in the training set come from the same distribution as that of the target population, and that the cost of misclassification into different classes are the same. However, these assumptions are often violated in real world settings. For some classification methods, this can often be taken care of simply with a change of threshold; for others, additional effort is required. In this paper, we explain why the standard support vector machine is not suitable for the nonstandard situation, and introduce a simple procedure for adapting the support vector machine methodology to the nonstandard situation. Theoretical justification for the procedure is provided. Simulation study illustrates that the modified support vector machine significantly improves upon the standard support vector machine in the nonstandard situation. The computational load of the proposed procedure is the same as that of the standard support vector machine. The procedure reduces to the standard support vector machine in the standard situation.
2928406
Tuning Schema Matching Software using Synthetic Scenarios
Most recent schema matching systems assemble multiple components, each employing a particular matching technique. The domain user must then tune the system: select the right component to be executed and correctly adjust their numerous "knobs" (e.g., thresholds, formula coefficients). Tuning is skill- and time-intensive, but (as we show) without it the matching accuracy is significantly inferior.We describe eTuner, an approach to automatically tune schema matching systems. Given a schema S, we match S against synthetic schemas, for which the ground truth mapping is known, and find a tuning that demonstrably improves the performance of matching S against real schemas. To efficiently search the huge space of tuning configurations, eTuner works sequentially, starting with tuning the lowest level components. To increase the applicability of eTuner, we develop methods to tune a broad range of matching components. While the tuning process is completely automatic, eTuner can also exploit user assistance (whenever available) to further improve the tuning quality. We employed eTuner to tune four recently developed matching systems on several real-world domains. eTuner produced tuned matching systems that achieve higher accuracy than using the systems with currently possible tuning methods, at virtually no cost to the domain user.
12255146
On robust transaction routing and load sharing
In this paper we examine the issue of robust transaction routing in a locally distributed database environment where transaction characteristics such as reference locality imply that certain processing systems can be identified as being more suitable than others for a given transaction class. A response time based routing strategy can strike a balance between indiscriminate sharing of the load and routing based only on transaction affinity. Since response time estimates depend on workload and system parameters that may not be readily available, it is important to examine the robustness of routing decisions to information accuracy. We find that a strategy which strictly tries to minimize the response time of incoming transactions is sensitive to the accuracy of certain parameter values. On the other hand, naive strategies, that simply ignore the parameters in making routing decisions, have even worse performance. Three alternative strategies are therefore examined: threshold, discriminatory, and adaptive. Instead of just optimizing an incoming transaction's response time, the first two strategies pursue a strategy that is somewhat more oriented towards global optimization. This is achieved by being more restrictive on either the condition or the candidate for balancing the load. The third strategy, while trying to minimize the response time of individual incoming transactions, employs a feedback process to adaptively adjust future response time estimates. It monitors the discrepancy between the actual and estimated response times and introduces a correction factor based on regression analysis. All three strategies are shown to be robust with respect to the accuracy of workload and system parameters used in the response time estimation.
5304492
Design specifications of a generic supply chain simulator
This paper describes a design specification for a generic, supply-chain-simulation system. The proposed simulation system is based on schedule-driven and stock-driven control methods to support the supply chain management. The simulation system includes three processing modes: business process flows, material process flows, and information process flows. The paper also discusses interface data requirements for the proposed supply-chain-simulation system.
2908844
Operation-based Update Propagation in a Mobile File System
In this paper we describe a technique called operation-based update propagation for efficiently transmitting updates to large files that have been modified on a weakly connected client of a distributed file system. In this technique, modifications are captured above the file-system layer at the client, shipped to a surrogate client that is strongly connected to a server, re-executed at the surrogate, and the resulting files transmitted from the surrogate to the server. If re-execution fails to produce a file identical to the original, the system falls back to shipping the file from the client over the slow network. We have implemented a prototype of this mechanism in the Coda File System on Linux, and demonstrated performance improvements ranging from 40 percents to nearly three orders of magnitude in reduced network traffic and elapsed time. We also found a novel use of forward error correction in this context.
17546848
Analysis of Fuzzy Operators for High Quality Information Retrieval
Kim, M.H., J.H. Lee and Y.J. Lee, Analysis of fuzzy operators for high quality information retrieval. Information Processing Letters 46 (1993) 251-256. It has been argued that the conventional fuzzy set model based on the MIN and MAX operators is not appropriate for a model of information retrieval systems due to the properties of the MIN and MAX operators adverse to retrieval effectiveness. In this paper we investigate the behavioral aspects of various fuzzy operators and address the properties which are important in achieving high quality document rankings. We then present the fuzzy set model based on positively compensatory operators, which provides higher retrieval effectiveness than the conventional fuzzy set model. It is also described that the behavior of the proposed fuzzy set model almost coincides with that of the extended boolean model.