Estimating RASATI scores using acoustical parameters
نویسندگان
چکیده
منابع مشابه
Estimating Comparable Scores Using Surrogate Variables
can be found at: Applied Psychological Measurement Additional services and information for http://apm.sagepub.com/cgi/alerts Email Alerts: http://apm.sagepub.com/subscriptions Subscriptions: http://www.sagepub.com/journalsReprints.nav Reprints: http://www.sagepub.com/journalsPermissions.nav Permissions: http://apm.sagepub.com/cgi/content/refs/25/2/197 SAGE Journals Online and HighWire Press p...
متن کاملestimating claims reserves using ibnr reserves
شرکت های بیمه در قبال بیمه گذاران متعهد می شوند که به ازای دریافت حق بیمه، خسارت وارده به بیمه گذاران را جبران نمایند. بنابراین بیمه گر همیشه مبالغی را بر عهده دارد که متعلق به بیمه گذاران است و مربوط به تعهدات آینده می باشد. این مبالغ را ذخایر فنی یا technical reserves گویند. بیمه گر موظف است در پایان سال مالی، هنگام بستن حساب ها، ذخایر فنی را محاسبه و نگهداری کند. پس از وقوع خسارت، بیمه گذار ...
Estimating Underwater Acoustical Parameters from Space-based Synthetic Aperture Radar Imagery
Synthetic aperture radar (SAR) imagery from satellites provides a range of data products that can reveal certain aspects of the underwater acoustical environment. Space-based SAR imagery is available in all weather, day or night and has wide area coverage with swaths up to 500 km across. This availability and coverage gives SAR satellite data the potential to enhance acoustic environmental asse...
متن کاملEstimating Grammar Parameters Using Bounded Memory
Estimating the parameters of stochastic context-free grammars (SCFGs) from data is an important, well-studied problem. Almost without exception, existing approaches make repeated passes over the training data. The memory requirements of such algorithms are illsuited for embedded agents exposed to large amounts of training data over long periods of time. We present a novel algorithm, called HOLA...
متن کاملEstimating Grammar Parameters using Bounded Memory
Estimating the parameters of stochastic context-free grammars (SCFGs) from data (i.e., strings) is an important, well-studied problem. Almost without exception, existing approaches make repeated passes over the training data. The memory requirements of such algorithms are ill-suited for embedded agents exposed to large amounts of training data over long periods of time. We present a novel algor...
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: Journal of Physics: Conference Series
سال: 2011
ISSN: 1742-6596
DOI: 10.1088/1742-6596/332/1/012050