| Background | 3.3 Support vector machines |
| Background | Support vector machines (SVMs) are pattern classification methods that aim to find an optimal separating hyperplane between examples from two different classes (Shawe-Taylor and Cristianini, 2004). |
| Background | that is, a linear function over (a subset of) training examples, where 04,- is the weight associated with training example 2' (those for which a, > 0 are the so called support vectors ) and y,- is the label associated with training example i, K (xi, xj) is a kernel2 function that aims at mapping the input vectors, (xi, xj), into the so called feature space, and b is a bias term. |
| Introduction | 0 We study several kernels for a support vector machine AA classifier under the local histograms formulation. |
| Related Work | applied to this problem, including support vector machine (SVM) classifiers (Houvardas and Stamatatos, 2006) and variants thereon (Plakias and Stamatatos, 2008b; Plakias and Stamatatos, 2008a), neural networks (Tearle et al., 2008), Bayesian classifiers (Coyotl-Morales et al., 2006), decision tree methods (Koppel et al., 2009) and similarity based techniques (Keselj et al., 2003; Lambers and Veenman, 2009; Stamatatos, 2009b; Koppel et al., 2009). |