Feature space approximation for kernel-based supervised learning.
Patrick GelßStefan KlusIngmar SchusterChristof SchüttePublished in: CoRR (2020)
Keyphrases
- feature space
- supervised learning
- kernel methods
- training set
- training samples
- support vector machine
- kernel pca
- learning algorithm
- high dimensional
- learning problems
- feature vectors
- classification accuracy
- kernel function
- semi supervised learning
- approximation error
- unsupervised learning
- class labels
- active learning
- low dimensional
- kernel trick
- reinforcement learning
- statistical learning
- data points
- learning tasks
- error bounds
- input space
- feature set
- machine learning
- training data
- principal component analysis
- hyperplane
- mercer kernel
- mean shift
- semi supervised
- image representation
- generalization error
- multiple kernel learning
- unlabeled data
- nonlinear kernel
- online learning