A New Frontier in Information System Research-The Support Vector Regression Approach

資訊系統研究方法的新領域－支援向量迴歸方法

李慶章(Ching-Chang Lee) ； 董信煌(Shing-Hwang Doong)

《資訊管理學報》 13卷3期 (2006/07) Pp. 217-237

https://doi.org/10.6382/JIM.200607.0217

摘要

本研究提出一個創新的支援向量迴歸方法來探討應用任務科技適配理論於資訊系統採用之問題。支援向量迴歸方法可以在給定的資料中產生一個簡潔的迴歸模式，以避免傳統機器學習法中的資料過度學習問題。根基於統計學習、數學規劃及氾函分析理論，支援向量迴歸方法較傳統的多元迴歸方法在迴歸正確性上有較好的成效。本研究中我們也利用拔靴法設計出一個由支援向量方法萃取顯著因子的步驟。

關鍵字

拔靴法 ； 支援向量迴歸 ； 無母數迴歸 ； 任務科技適配 ； 行動商務

並列摘要

In this study, we propose a novel application of the Support Vector Regression (SVR) method to model a task variable in the Task-Technology Fit (TTF) theory. The support vector approach learns a parsimonious regression model from the given data to avoid the data over-fitting problem. Founded on the theories of statistical learning, mathematical programming and functional analysis, SVR is shown to outperform the traditional multiple linear regression method from the perspective of regression accuracy. Using a bootstrap procedure, we design a mechanism to extract significant factors from the support vector approach.

並列關鍵字

Bootstrap ； Support vector regression ； Nonparametric regression ； Task-technology fit ； Mobile commerce

參考文獻

Ait-Sahalia, Y.,Bickel, P.,Stoker, T.(2001).Goodness-of-fit tests for kernel regression with and application to option implied volatilities.Journal of Econometrics.105,363-412.

LIBSVM: a library for support vector machines, 2001

Chin, K. K.(1999).Support Vector Machines applied to Speech Pattern Classification.Engineering Department, Cambridge University.

Cristianini, N.,Shawe-Taylor, J.(2000).Support Vector Machines and Other Kernel Based Leaning Methods.Cambridge University Press.

Devore, J.(2004).Probability and Statistics for Engineering and the Science.Belmont, CA:Thomson Brooks/Cole.