Computer science Stochastic m-estimators| Controlling accuracy-cost tradeoffs in machine learning GEORGIA INSTITUTE OF TECHNOLOGY Guy Lebanon DillonJoshua Vm-Estimation represents a broad class of estimators, including least-squares and maximum likelihood, and is a widely used tool for statistical ...
Any machine learning project consists of many stages that include training, evaluation, prediction, and finally exporting it for serving on a production server. You learned these stages in previous chapters where the classification and regression machine learning projects were discussed. To develop the ...
- 《Journal of Machine Learning Research》 被引量: 47发表: 2014年 Empirical Bayes Estimation of Small Area Means under a Nested Error Linear Regression Model with Measurement Errors in the Covariates mean squared prediction errornested error modelPreviously, small area estimation under a nested error...
National statistical organizations often rely on non-exhaustive surveys to estimate industry-level production functions in years in which a full census is not conducted. When analyzing data from non-census years, we propose selecting an estimator based on a weighting of its in-sample and predictive...
However, their statistical properties are not well understood, in theory. And in practice, avoiding numerical instability requires careful tuning of key parameters. Here, we introduce implicit stochastic gradient descent procedures, which involve parameter updates that are implicitly defined. Intuitively, ...
Estimators: A high-level way to create TensorFlow models. Estimators include pre-made models for common machine learning tasks, but you can also use them to create your own custom models. Below you can see how they fit in the TensorFlow architecture. Combined, they offer an easy way to crea...
A common problem in econometrics, statistics, and machine learning is to estimate and make inference on functions that satisfy shape restrictions. For exam... X Chen,I Fern\\'Andezval,Y Luo 被引量: 0发表: 2021年 Point and Interval Estimators of an Indirect Effect for a Binary Outcome A ...
Mean Squared error matrixIn the presence of stochastic prior information, in addition to the sample, Theil and Goldberger (1961) introduced a Mixed ... MH Hubert,P Wijekoon - 《Statistical Papers》 被引量: 91发表: 2006年 Improved Estimation in Measurement Error Models Through Stein Rule Procedur...
usingSystem;usingMicrosoft.ML;usingMicrosoft.ML.Data;usingMicrosoft.ML.Transforms.Image;namespaceSamples.Dynamic{classConvertToGrayScaleInMemory{publicstaticvoidExample(){varmlContext =newMLContext();// Create an image list.varimages =new[] {newImageDataPoint(2,3, red:0, green:0, blue:255),/...
In Proceedings of the 18th international joint conference on Artificial intelligence, Morgan Kaufmann (pp. 587–592). Li, X.L., & Liu, B. (2005). Learning from positive and unlabeled examples with different data distributions. In Proceedings of the European Conference on Machine Learning, ...