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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Optimal semi-supervised subsampling via predictive inference
预测推理 最优 半监督子采样
2023/4/26
In big data era, subsampling or sub-data selection techniques are often adopted to extract a fraction of informative individuals from the massive data. Existing subsampling algorithms focus mainly on ...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Fusion of Supervised Learning and Reinforcement Learning for Dynamic Treatment Recommendation
监督学习 强化学习 融合 动态治疗推荐
2023/5/5
Electronic health records (EHR) have provided a great opportunity to exploit personalized health data to optimize clinical decision making and achieve personalized treatment recommendation. In this ta...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars： Fusion of Supervised Learning and Reinforcement Learning for Dynamic Treatment Recommendation
监督学习 强化学习 融合 动态治疗推荐 电子健康记录
2023/5/6
Electronic health records (EHR) have provided a great opportunity to exploit personalized health data to optimize clinical decision making and achieve personalized treatment recommendation. In this ta...
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning
Inferring Label Sampling Mechanisms Semi-Supervised Learning
2015/8/21
We consider the situation in semi-supervised learning, where the “label sampling” mechanism stochastically depends on the true response (as well as potentially on the features). We suggest a method of...
Prediction by Supervised Principal Components
Gene expression Microarray Regression Survival analysis
2015/8/21
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called ...
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called ...
SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity
Semi-supervised Metric Learning Paradigm Hyper Sparsity
2011/6/15
We consider the problem of learning a distance metric from a limited amount of pairwise information as effectively as possible. The proposed SERAPH (SEmi-supervised metRic leArning Paradigm with Hyper...
Safe Feature Elimination in Sparse Supervised Learning
Sparse classi cation sparse regression LASSO feature elimination
2010/12/16
We investigate fast methods that allow to quickly eliminate variables (features) in supervised
learning problems involving a convex loss function and a l1-norm penalty, leading to a potentially subst...