Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Recognition of On-line Handwritten Mathematical Formulas in the E-Chalk System
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Similarity-driven Sequence Classification Based on Support Vector Machines
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Prominent streak discovery in sequence data
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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A key challenge in applying kernel-based methods for discriminative learning is to identify a suitable kernel given a problem domain. Many methods instead transform the input data into a set of vectors in a feature space and classify the transformed data using a generic kernel. However, finding an effective transformation scheme for sequence (e.g. time series) data is a difficult task. In this paper, we introduce a scheme for directly designing kernels for the classification of sequence data such as that in handwritten character recognition and object recognition from sensor readings. Ordering information is represented by values of a parameter associated with each input data element. A similarity metric based on the parametric distance between corresponding elements is combined with their problemspecific similarity metric to produce a Mercer kernel suitable for use in methods such as support vector machine (SVM). This scheme directly embeds extraction of features from sequences of varying cardinalities into the kernel without needing to transform all input data into a common feature space before classification. We apply our method to object and handwritten character recognition tasks and compare against current approaches. The results show that we can obtain at least comparable accuracy to state of the art problem-specific methods using a systematic approach to kernel design. Our contribution is the introduction of a general technique for designing SVM kernels tailored for the classification of sequence data.