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Yaakov Engel
Researcher at Rafael Advanced Defense Systems
Publications - 17
Citations - 2315
Yaakov Engel is an academic researcher from Rafael Advanced Defense Systems. The author has contributed to research in topics: Gaussian process & Kernel (statistics). The author has an hindex of 12, co-authored 17 publications receiving 2109 citations. Previous affiliations of Yaakov Engel include University of Alberta & Interdisciplinary Center for Neural Computation.
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Journal ArticleDOI
The kernel recursive least-squares algorithm
TL;DR: A nonlinear version of the recursive least squares (RLS) algorithm that uses a sequential sparsification process that admits into the kernel representation a new input sample only if its feature space image cannot be sufficiently well approximated by combining the images of previously admitted samples.
Proceedings ArticleDOI
Reinforcement learning with Gaussian processes
TL;DR: A SARSA based extension of GPTD is presented, termed GPSARSA, that allows the selection of actions and the gradual improvement of policies without requiring a world-model.
Proceedings Article
Bayes meets bellman: the Gaussian process approach to temporal difference learning
TL;DR: A novel Bayesian approach to the problem of value function estimation in continuous state spaces by imposing a Gaussian prior over value functions and assuming aGaussian noise model is presented.
Journal ArticleDOI
Dynamic Model of the Octopus Arm. I. Biomechanics of the Octopus Reaching Movement
TL;DR: A dynamic model of the octopus arm is presented to explore possible strategies of movement control in this muscular hydrostat and finds that a simple command producing a wave of muscle activation moving at a constant velocity is sufficient to replicate the natural reaching movements with similar kinematic features.
Journal Article
Sparse online greedy support vector regression
TL;DR: In this article, a sparse online greedy kernel-based nonlinear regression algorithm is proposed, which admits a new input sample only if its feature space image is linearly independent of the images of previously admitted samples.