Support vector machine for functional data classification
Fabrice Rossi,Nathalie Villa +1 more
TLDR
In this article, the authors investigate the use of support vector machines (SVM) for functional data analysis (FDA) and focus on the problem of curve discrimination and define simple kernels that take into account the functional nature of the data and lead to consistent classification.About:
This article is published in Neurocomputing.The article was published on 2006-03-01 and is currently open access. It has received 198 citations till now. The article focuses on the topics: Data classification & Relevance vector machine.read more
Citations
More filters
Journal ArticleDOI
Permutation Tests for Studying Classifier Performance
Markus Ojala,Gemma C. Garriga +1 more
TL;DR: The analysis shows that studying the classification error via permutation tests is effective; in particular, the restricted permutation test clearly reveals whether the classifier exploits the interdependency between the features in the data.
Proceedings ArticleDOI
Permutation Tests for Studying Classifier Performance
Markus Ojala,Gemma C. Garriga +1 more
TL;DR: In this paper, the authors explore the framework of permutation-based p-values for assessing the behavior of the classification error and study two simple permutation tests: the first test estimates the null distribution by permuting the labels in the data; this has been used extensively in classification problems in computational biology and the second test produces permutations of the features within classes, inspired by restricted randomization techniques traditionally used in statistics.
Journal ArticleDOI
Achieving near perfect classification for functional data
Aurore Delaigle,Peter Hall +1 more
TL;DR: It is shown that, in functional data classification problems, perfect asymptotic classification is often possible, making use of the intrinsic very high dimensional nature of functional data, which points to a marked contrast between classification for functional data and its counterpart in conventional multivariate analysis.
Journal ArticleDOI
Grouped variable importance with random forests and application to multiple functional data analysis
TL;DR: An original method for selecting functional variables based on the grouped variable importance measure is developed and it is proposed to regroup all of the wavelet coefficients for a given functional variable and use a wrapper selection algorithm with these groups.
Journal ArticleDOI
Travel Mode Choice Modeling with Support Vector Machines
TL;DR: This study investigates the applications of nontraditional models for travel mode choice modeling, which traditionally has relied on disaggregate discrete choice models such as multinomial logit models, and recommends the support vector machine model for its promising performance and easy implementation.
References
More filters
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.