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We propose a landmine detection algorithm using ground penetrating radar data that is based on an SVM classifier. The kernel function for the SVM is constructed using discrete hidden Markov modeling (HMM). Typically, the kernel matrix could be obtained by defining an adequate similarity measure in the feature space. However, this approach is inappropriate as it is not trivial to define a meaningful distance metric for sequence comparison. Our proposed approach is based on HMM modeling and has two main steps. First, one HMM is fit to each of the N individual sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an N × N log-likelihood similarity matrix that will be adapted to serve as the kernel of the SVM classifier. In the second step, we train an SVM classifier to learn a decision boundary between the positive and negative samples.