Impartial trimmed k-means for functional data
Computational Statistics & Data Analysis
Classifying densities using functional regression trees: Applications in oceanology
Computational Statistics & Data Analysis
Classification of functional data: A segmentation approach
Computational Statistics & Data Analysis
Additive prediction and boosting for functional data
Computational Statistics & Data Analysis
Support vector machine for functional data classification
Neurocomputing
A half-region depth for functional data
Computational Statistics & Data Analysis
Identifying cluster number for subspace projected functional data clustering
Computational Statistics & Data Analysis
Functional density synchronization
Computational Statistics & Data Analysis
Polarization of forecast densities: A new approach to time series classification
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
A natural methodology for discriminating functional data is based on the distances from the observation or its derivatives to group representative functions (usually the mean) or their derivatives. It is proposed to use a combination of these distances for supervised classification. Simulation studies show that this procedure performs very well, resulting in smaller testing classification errors. Applications to real data show that this technique behaves as well as-and in some cases better than-existing supervised classification methods for functions.