Predicting particle accelerator failures using binary classifiers
نویسندگان
چکیده
منابع مشابه
Predicting Healthcare Costs Using Classifiers
In the battle to control escalating health care costs, predictive models are increasingly employed to better allocate health care resources and to identify the “best” cases for preventive case management. In this investigation we predicted the top 0.5% most costly cases for year N+1, given a population in year N , with data for the period 1997-2001 taken from the MEDSTAT Marketscan Research Dat...
متن کاملText Dependent Speaker Verification Using Binary Classifiers Text Dependent Speaker Verification Using Binary Classifiers
This paper describes how a speaker veri cation task can be advantageously decom posed into a series of binary classi cation problems i e each problem discriminating between two classes only Each binary classi er is speci c to one speaker one anti speaker and one word De cision trees dealing with attributes of continuous values are used as classi ers The set of classi ers is then pruned to elimi...
متن کاملOptimal threshold estimation for binary classifiers using
Many bioinformatics algorithms can be understood as binary classifiers. They are usually compared using the area under the receiver operating characteristic ( ) curve. On the other hand, choosing the best threshold for practical use ROC is a complex task, due to uncertain and context-dependent skews in the abundance of positives in nature and in the yields/costs for correct/incorrect classifica...
متن کاملText dependent speaker verification using binary classifiers
This paper describes how a speaker verification task can be advantageously decomposed into a series of binary classification problems, i.e. each problem discriminating between two classes only. Each binary classifier is specific to one speaker, one anti-speaker and one word. Decision trees dealing with attributes of continuous values are used as classifiers. The set of classifiers is then prune...
متن کاملPredicting Human Assessment of Machine Translation Quality by Combining Automatic Evaluation Metrics using Binary Classifiers
This paper presents a method to predict human assessments of machine translation (MT) quality based on a combination of binary classifiers using a coding matrix. The multiclass categorization problem is reduced to a set of binary problems that are solved using standard classification learning algorithms trained on the results of multiple automatic evaluation metrics. Experimental results using ...
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: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
سال: 2020
ISSN: 0168-9002
DOI: 10.1016/j.nima.2019.163240