Appropriate kernel functions for support vector machine learning with sequences of symbolic data

Authors:
Bram Vanschoenwinkel;Bernard Manderick
Affiliations:
Computational Modeling Lab, Vrije Universiteit Brussel, Brussel, Belgium;Computational Modeling Lab, Vrije Universiteit Brussel, Brussel, Belgium
Venue:
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Year:
2004

Citing 10
Cited 3

The nature of statistical learning theory

The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms

Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Syllables and other String Kernel Extensions

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Comparison and classification of dialects

EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Profile-Based String Kernels for Remote Homology Detection and Motif Extraction

CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Use of support vector learning for chunk identification

ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Entity extraction without language-specific resources

COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Use of support vector machines in extended named entity recognition

COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
A weighted polynomial information gain kernel for resolving prepositional phrase attachment ambiguities with support vector machines

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence

Learning to recognize faces incrementally

Proceedings of the 29th DAGM conference on Pattern recognition
Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data

The Journal of Machine Learning Research
Context-Sensitive kernel functions: a distance function viewpoint

ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In classification problems, machine learning algorithms often make use of the assumption that (dis)similar inputs lead to (dis)similar outputs. In this case, two questions naturally arise: what does it mean for two inputs to be similar and how can this be used in a learning algorithm? In support vector machines, similarity between input examples is implicitly expressed by a kernel function that calculates inner products in the feature space. For numerical input examples the concept of an inner product is easy to define, for discrete structures like sequences of symbolic data however these concepts are less obvious. This article describes an approach to SVM learning for symbolic data that can serve as an alternative to the bag-of-words approach under certain circumstances. This latter approach first transforms symbolic data to vectors of numerical data which are then used as arguments for one of the standard kernel functions. In contrast, we will propose kernels that operate on the symbolic data directly.