Algorithms for clustering data
Algorithms for clustering data
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Model-Based Distance for Clustering
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Asymptotic properties of the Fisher kernel
Neural Computation
Robust Word Similarity Estimation Using Perturbation Kernels
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
A comparative study on the use of labeled and unlabeled data for large margin classifiers
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
| Hi-index | 0.00 |
Recently, several attempts have been made for deriving datadependent kernels from distribution estimates withparametric models (e.g. the Fisher kernel). In this paper, we propose a new kernel derived from any distribution estimators, parametric or nonparametric. This kernel is called the Leave-one-out kernel (i.e. LOO kernel), because the leave-one-out process plays an important role to compute this kernel. We will show that, when applied to a parametric model, the LOO kernel converges to the Fisher kernel asymptotically as the number of samples goes to infinity.