Better models by discarding data?
نویسندگان
چکیده
منابع مشابه
Better models by discarding data?
In macromolecular X-ray crystallography, typical data sets have substantial multiplicity. This can be used to calculate the consistency of repeated measurements and thereby assess data quality. Recently, the properties of a correlation coefficient, CC1/2, that can be used for this purpose were characterized and it was shown that CC1/2 has superior properties compared with `merging' R values. A ...
متن کاملDiscarding impossible events from statistical language models
This paper describes a method for detecting impossible bigrams from a space of V 2 bigrams where V is the size of the vocabulary. The idea is to discard all the ungrammatical events which are impossible in a well written text and consequently to expect an improvement of the language model. We expect also, in speech recognition, to reduce the complexity of the search algorithm by making less com...
متن کاملmodeling loss data by phase-type distribution
بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...
Discarding data may help in system identification
We present results concerning the parameter estimates obtained by prediction error methods in the case of input signals that are insuuciently rich. Such input signals are typical of industrial measurements where occasional stepwise reference changes occur. As is intuitively obvious, the data located around the input signal discontinuities carry most of the useful information. Using singular val...
متن کاملInformation-Theoretic Data Discarding for Dynamic Trees on Data Streams
Ubiquitous automated data collection at an unprecedented scale is making available streaming, real-time information flows in a wide variety of settings, transforming both science and industry. Learning algorithms deployed in such contexts often rely on single-pass inference, where the data history is never revisited. Learning may also need to be temporally adaptive to remain up-to-date against ...
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: Acta Crystallographica Section D Biological Crystallography
سال: 2013
ISSN: 0907-4449
DOI: 10.1107/s0907444913001121