Learning from local to global: An efficient distributed algorithm for modeling time-to-event data.
Rui DuanChongliang LuoMartijn J. SchuemieJiayi TongC. Jason LiangHoward H. ChangMary Regina BolandJiang BianHua XuJohn H. HolmesChristopher B. ForrestSally C. MortonJesse A. BerlinJason H. MooreKevin B. MahoneyYong ChenPublished in: J. Am. Medical Informatics Assoc. (2020)
Keyphrases
- learning algorithm
- input data
- learned models
- data sets
- data reduction
- noisy data
- data analysis
- training data
- optimal solution
- computational complexity
- preprocessing
- synthetic datasets
- detection algorithm
- prior knowledge
- knowledge discovery
- dynamic programming
- reinforcement learning
- data distribution
- bayesian methods
- global information
- np hard
- distributed data
- worst case
- simulated annealing
- learning phase
- machine learning
- segmentation algorithm
- k means
- search space
- database
- similarity measure
- spectral clustering
- remote sites
- background knowledge
- clustering method
- data mining techniques
- probability distribution
- probabilistic model
- xml documents
- lower bound
- neural network