Sensing-Aware Kernel SVM
This work addresses kernel design for SVMs in classification tasks with latent states, offering a method that can match sophisticated state-of-the-art approaches, though it appears incremental as it builds on existing SVM and sensing model frameworks.
The paper tackled the problem of designing kernels for support vector machines when class labels depend on latent states and a sensing model is available, showing that the Bayes-optimum decision boundary is a hyperplane under a likelihood-based mapping and deriving an optimum kernel for bag-of-words models that outperforms other kernels in document and image classification tasks.
We propose a novel approach for designing kernels for support vector machines (SVMs) when the class label is linked to the observation through a latent state and the likelihood function of the observation given the state (the sensing model) is available. We show that the Bayes-optimum decision boundary is a hyperplane under a mapping defined by the likelihood function. Combining this with the maximum margin principle yields kernels for SVMs that leverage knowledge of the sensing model in an optimal way. We derive the optimum kernel for the bag-of-words (BoWs) sensing model and demonstrate its superior performance over other kernels in document and image classification tasks. These results indicate that such optimum sensing-aware kernel SVMs can match the performance of rather sophisticated state-of-the-art approaches.