1 citations · 1 across the 8 of their papers we have counts for
8 papers
Practical machine learning is learning on small samples
Marina Sapir
Based on limited observations, machine learning discerns a dependence which is expected to hold in the future. What makes it possible? Statistical learning theory imagines indefini…
Theory of Machine Learning with Limited Data
Marina Sapir
Application of machine learning may be understood as deriving new knowledge for practical use through explaining accumulated observations, training set. Peirce used the term abduct…
Logic of Machine Learning
Marina Sapir
The main question is: why and how can we ever predict based on a finite sample? The question is not answered by statistical learning theory. Here, I suggest that prediction require…
Learnable: Theory vs Applications
Marina Sapir
Two different views on machine learning problem: Applied learning (machine learning with business applications) and Agnostic PAC learning are formalized and compared here. I show t…
Optimal choice: new machine learning problem and its solution
Marina Sapir
The task of learning to pick a single preferred example out a finite set of examples, an "optimal choice problem", is a supervised machine learning problem with complex, structured…
Bipartite ranking algorithm for classification and survival analysis
Marina Sapir
Unsupervised aggregation of independently built univariate predictors is explored as an alternative regularization approach for noisy, sparse datasets. Bipartite ranking algorithm…
Bias Plus Variance Decomposition for Survival Analysis Problems
Marina Sapir
Bias - variance decomposition of the expected error defined for regression and classification problems is an important tool to study and compare different algorithms, to find the b…
Ensemble Risk Modeling Method for Robust Learning on Scarce Data
Marina Sapir
In medical risk modeling, typical data are "scarce": they have relatively small number of training instances (N), censoring, and high dimensionality (M). We show that the problem m…