Learning a kernel function for classification with small training samples Conference Paper uri icon

abstract

  • When given a small sample, we show that classification with SVM can be considerably enhanced by using a kernel function learned from the training data prior to discrimination. This kernel is also shown to enhance retrieval based on data similarity. Specifically, we describe KernelBoost-a boosting algorithm which computes a kernel function as a combination of'weak'space partitions. The kernel learning method naturally incorporates domain knowledge in the form of unlabeled data (ie in a semi-supervised or transductive settings), and also in the form of labeled samples from relevant related problems (ie in a learning-to-learn scenario). The latter goal is accomplished by learning a single kernel function for all classes. We show comparative evaluations of our method on datasets from the UCI repository. We demonstrate performance enhancement on two challenging tasks …

publication date

  • January 1, 2006