Improved supervised learning methods for EoR parameters reconstruction
نویسندگان
چکیده
منابع مشابه
Approximation Methods for Supervised Learning
Let ρ be an unknown Borel measure defined on the space Z := X × Y with X ⊂ IR and Y = [−M,M ]. Given a set z ofm samples zi = (xi, yi) drawn according to ρ, the problem of estimating a regression function fρ using these samples is considered. The main focus is to understand what is the rate of approximation, measured either in expectation or probability, that can be obtained under a given prior...
متن کاملMathematical Methods for Supervised Learning
Let ρ be an unknown Borel measure defined on the space Z := X × Y with X ⊂ IR and Y = [−M,M ]. Given a set z ofm samples zi = (xi, yi) drawn according to ρ, the problem of estimating a regression function fρ using these samples is considered. The main focus is to understand what is the rate of approximation, measured either in expectation or probability, that can be obtained under a given prior...
متن کاملCMSC702 Supervised Learning Methods
Today we discuss the classification setting in detail. Our setting is that we observe for each subject i a set of p predictors (covariates) xi, and qualitative outcomes (or classes) gi, which can takes values from a discrete set G. In this class we assume that predictors are genomic measurements (gene expression for now), and that we have many more measurements than samples (i.e. p << N , where...
متن کاملSemi - supervised Learning Methods for Data Augmentation
The original goal of this project was to investigate the extent to which data augmentation schemes based on semi-supervised learning algorithms can improve classification accuracy in supervised learning problems. The objectives included determining the appropriate algorithms, customising them for the purposes of this project and providing their Matlab implementations. These algorithms were to b...
متن کاملSupervised Machine Learning Methods for Item Recommendation
class, 107accuracy, 40AdaBoost, 77adaptivity, 41age, 81ALS, see alternating least squaresalternating least squares, 36, 86Apache Mahout, 86area under the ROC curve, 41, 61, 82aspect model, 58association rules, 87attribute-based kNN, 81attribute-to-factor mapping, 45 59AUC, see area under the ROC curve bagging, 77, 87Bayesian Context-Aware ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Monthly Notices of the Royal Astronomical Society
سال: 2019
ISSN: 0035-8711,1365-2966
DOI: 10.1093/mnras/stz2429