Đang chuẩn bị liên kết để tải về tài liệu:
A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard

Không đóng trình duyệt đến khi xuất hiện nút TẢI XUỐNG

Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. |

TÀI LIỆU LIÊN QUAN

Báo cáo khoa học: "A Bayesian Method for Robust Estimation of Distributional Similarities"

Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models

Confident difference criterion: A new Bayesian differentially expressed gene selection algorithm with applications

A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits

A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard

ALPHLARD: A Bayesian method for analyzing HLA genes from whole genome sequence data

Epistasis analysis of microRNAs on pathological stages in colon cancer based on an Empirical Bayesian Elastic Net method

An improved Bayesian network method for reconstructing gene regulatory network based on candidate auto selection

Nuclear data correlation between different isotopes via integral information

Bayesian optimization of generalized data
Đã phát hiện trình chặn quảng cáo AdBlock
Trang web này phụ thuộc vào doanh thu từ số lần hiển thị quảng cáo để tồn tại. Vui lòng tắt trình chặn quảng cáo của bạn hoặc tạm dừng tính năng chặn quảng cáo cho trang web này.