Weak convergence and optimal tuning of the reversible jump algorithm.
Philippe GagnonMylène BédardAlain DesgagnéPublished in: Math. Comput. Simul. (2019)
Keyphrases
- dynamic programming
- optimal solution
- detection algorithm
- worst case
- theoretical analysis
- learning algorithm
- markov chain
- high accuracy
- np hard
- preprocessing
- k means
- times faster
- segmentation algorithm
- experimental evaluation
- computational cost
- parameter tuning
- space complexity
- exhaustive search
- globally optimal
- convergence rate
- computational complexity
- neural network
- iterative algorithms
- computationally efficient
- parameter settings
- improved algorithm
- convergence property
- fine tuning
- convergence proof
- convergence analysis
- number of iterations required
- recognition algorithm
- matching algorithm
- similarity measure
- reinforcement learning
- objective function
- data structure
- cost function