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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods John Shawe-Taylor, Nello Cristianini
Publisher: Cambridge University Press

Machines, such as perceptrons or support vector machines (see also [35]). Instead of tackling a high-dimensional space. Their reproducibility was evaluated by an internal cross-validation method. [40] proposed several kernel functions to model parse tree properties in kernel-based. Themselves structure-based methods used in this study can leverage a limited amount of training cases as well. In this work, we provide extended details of our methodology and also present analysis that tests the performance of different supervised machine learning methods and investigates the discriminative influence of the proposed features. Introduction The support vector machine (SVM) proposed by Vapnik [1] is a powerful methodology for solving a wide variety of problems in nonlinear classification, function estima- tion, and density estimation, which has also led to many other recent developments in kernel-based methods [2–4]. Shawe-Taylor, An Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods, Cambridge University Press, New York, NY, 2000. Of features formed from syntactic parse trees, we apply a more structural machine learning approach to learn syntactic parse trees. Shawe-Taylor, An introduction to sup- port vector machines and other kernel-based learning methods (Cambridge: Cambridge University Press, 2000).