Mean Shift versus Variance Inflation Approach for Outlier Detection—A Comparative Study
نویسندگان
چکیده
منابع مشابه
A comparative Study of Outlier Mining and Class Outlier Mining
Outliers can significantly affect data mining performance. Outlier mining is an important issue in knowledge discovery and data mining and has attracted increasing interests in recent years. Class outlier is promising research direction. Few researches have been done in this direction. The paper theme has two main goals: the first one is to show the significance of Class Outlier Mining by discu...
متن کاملPortfolio-optimization by the mean-variance-approach
MaMaEuSch has been carried out with the partial support of the European Community in the framework of the Sokrates programme. The content does not necessarily reflect the position of the European Community, nor does it involve any responsibility on the part of the European Community.
متن کاملMean-Shift Algorithm: Verilog HDL Approach
Object tracking algorithms, when it comes to implementing it on hardware ASIC, it becomes difficult task, due to certain limitations in hardware. This paper shows how mean-shift algorithm is implemented in HDL along with the description of ports and interfaces. Keywords— Object tracking, complexity in hardware ASIC, Mean Shift algorithm, Histogram, Bhattacharya coefficient
متن کاملMean - Variance
We provide a new characterization of mean-variance hedging strategies in a general semimartingale market. The key point is the introduction of a new probability measure P ⋆ which turns the dynamic asset allocation problem into a myopic one. The minimal martingale measure relative to P ⋆ coincides with the variance-optimal martin-gale measure relative to the original probability measure P .
متن کاملComparative issues in large-scale mean-variance efficient frontier computation
Available online 25 November 2010
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: Mathematics
سال: 2020
ISSN: 2227-7390
DOI: 10.3390/math8060991