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
Support vector machines are universally consistent
Journal of Complexity
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Representation of functional data in neural networks
Neurocomputing
Functional classification in Hilbert spaces
IEEE Transactions on Information Theory
Artificial Intelligence in Medicine
Classification of gene functions using support vector machine for time-course gene expression data
Computational Statistics & Data Analysis
Classification of functional data: A segmentation approach
Computational Statistics & Data Analysis
TS-fuzzy system-based support vector regression
Fuzzy Sets and Systems
Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
The Representation of Chemical Spectral Data for Classification
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Functional classification of ornamental stone using machine learning techniques
Journal of Computational and Applied Mathematics
Permutation Tests for Studying Classifier Performance
The Journal of Machine Learning Research
On local times, density estimation and supervised classification from functional data
Journal of Multivariate Analysis
Consistency of functional learning methods based on derivatives
Pattern Recognition Letters
Functional data analysis in shape analysis
Computational Statistics & Data Analysis
Classification of three-way data by the dissimilarity representation
Signal Processing
A functional approach to variable selection in spectrometric problems
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Affective Interaction in Natural Environments
Supervised classification for functional data: A weighted distance approach
Computational Statistics & Data Analysis
Journal of Multivariate Analysis
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In many applications, input data are sampled functions taking their values in infinite-dimensional spaces rather than standard vectors. This fact has complex consequences on data analysis algorithms that motivate their modifications. In fact most of the traditional data analysis tools for regression, classification and clustering have been adapted to functional inputs under the general name of functional data analysis (FDA). In this paper, we investigate the use of support vector machines (SVMs) for FDA and we focus on the problem of curve discrimination. SVMs are large margin classifier tools based on implicit nonlinear mappings of the considered data into high-dimensional spaces thanks to kernels. We show how to define simple kernels that take into account the functional nature of the data and lead to consistent classification. Experiments conducted on real world data emphasize the benefit of taking into account some functional aspects of the problems.