DocumentCode
3203926
Title
A perceptron-like online algorithm for tracking the median
Author
Bylander, Tom ; Rosen, Bruce
Author_Institution
Div. of Comput. Sci., Texas Univ., San Antonio, TX, USA
Volume
4
fYear
1997
fDate
9-12 Jun 1997
Firstpage
2219
Abstract
We present an online algorithm for tracking the median of a series of values. The algorithm updates its current estimate of the median by incrementing or decrementing a fixed value, which is analogous to perceptron updating. The median value of a sequence minimizes the absolute loss, i.e., the sum of absolute deviations. The analysis shows that the worst-case absolute loss of our algorithm is comparable to the absolute loss of any sequence of target medians, given restrictions on how much the target can change per trial
Keywords
estimation theory; mathematics computing; minimisation; real-time systems; time series; absolute deviations; absolute loss; estimation theory; median tracking; online median algorithm; perceptron updating; time series; Algorithm design and analysis; Computer science; Fetal heart rate; Heart rate; Performance analysis; Probability distribution; Sampling methods; Sorting; Statistical distributions; Time series analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks,1997., International Conference on
Conference_Location
Houston, TX
Print_ISBN
0-7803-4122-8
Type
conf
DOI
10.1109/ICNN.1997.614292
Filename
614292
Link To Document