NIPS
2004
13 years 6 months ago
2004
In the context of binary classification, we define disagreement as a measure of how often two independently-trained models differ in their classification of unlabeled data. We exp...
ALENEX
2004
13 years 6 months ago
2004
We prove that the variance of the number of different letters in random words of length n, with letters i and probabilities 2-i attached to them, is 1 + o(1). Likewise, the varian...
IJCAI
2007
13 years 6 months ago
2007
— Calibrating the parameters of an evolutionary algorithm (EA) is a laborious task. The highly stochastic nature of an EA typically leads to a high variance of the measurements. ...
WSC
2008
13 years 7 months ago
2008
Estimating the variance of the sample mean from a stochastic process is essential in assessing the quality of using the sample mean to estimate the population mean which is the fu...
WSC
2007
13 years 7 months ago
2007
The Logistics Composite Model (LCOM) is a stochastic, discrete-event simulation that relies on probabilities and random number generators to model scenarios in a maintenance unit ...
CEC
2007
IEEE
13 years 9 months ago
2007
IEEE
Calibrating the parameters of an evolutionary algorithm (EA) is a laborious task. The highly stochastic nature of an EA typically leads to a high variance of the measurements. The ...
SIGGRAPH
1996
ACM
13 years 9 months ago
1996
ACM
Antialiased pixel values are often computed as the mean of N point samples. Using uniformly distributed random samples, the central limit theorem predicts a variance of the mean o...
ANSS
1996
IEEE
13 years 9 months ago
1996
IEEE
The asymptotic bias and variance are important determinants of the quality of a simulation run. In particular, the asymptotic bias can be used to approximate the bias introduced b...
SAC
2005
ACM
13 years 10 months ago
2005
ACM
: Traditional spatial indexes like R-tree usually assume the database is not updated frequently. In applications like location-based services and sensor networks, this assumption i...
GECCO
2007
Springer
13 years 11 months ago
2007
Springer
Recently, advances have been made in continuous, normal– distribution–based Estimation–of–Distribution Algorithms (EDAs) by scaling the variance up from the maximum–like...