SHIV: Reducing supervisor burden in DAgger using support vectors for efficient learning from demonstrations in high dimensional state spaces.
Michael LaskeySam StaszakWesley Yu-Shu HsiehJeffrey MahlerFlorian T. PokornyAnca D. DraganKen GoldbergPublished in: ICRA (2016)
Keyphrases
- efficient learning
- support vectors
- high dimensional
- input space
- kernel function
- training samples
- state space
- support vector
- support vector machine
- svm classifier
- low dimensional
- dimensionality reduction
- training examples
- hyperplane
- training data
- training dataset
- learning algorithm
- decision functions
- feature space
- high dimensional data
- nearest neighbor
- data points
- pattern languages
- membership queries
- neural network
- machine learning
- ls svm
- variable selection
- training process
- k nearest neighbor
- training set
- dimension reduction
- reinforcement learning
- face recognition
- knn
- support vector machine svm