Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
5,625,986
well-known
"... rubrum (Linné, 1758), belonging to the ordo Gorgonacea, is probably the most ..."
Abstract
- Add to MetaCart
rubrum (Linné, 1758), belonging to the ordo Gorgonacea, is probably the most
A database and evaluation methodology for optical flow
- In Proceedings of the IEEE International Conference on Computer Vision
, 2007
"... The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. The challenges for optical flow algorithms today go beyond the datasets and evaluation methods proposed in that paper. Instead, they center on problems associated with complex n ..."
Abstract
-
Cited by 404 (21 self)
- Add to MetaCart
by Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and results at motion discontinuities and in textureless regions. In October 2007, we published the performance of several well-known methods on a preliminary version of our data
The well-known formula:
"... Thermal verification of complex ICs can help the designer to detect if a particular block is working beyond specifications. A simple method is to extract the output frequencies of an array of ring-oscillators previously distributed in the die. The main advantage is that neither external transducers ..."
Abstract
- Add to MetaCart
Thermal verification of complex ICs can help the designer to detect if a particular block is working beyond specifications. A simple method is to extract the output frequencies of an array of ring-oscillators previously distributed in the die. The main advantage is that neither external transducers
Large margin methods for structured and interdependent output variables
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
Abstract
-
Cited by 612 (12 self)
- Add to MetaCart
to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm
Rank Aggregation Methods for the Web
, 2010
"... We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building meta-search engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations. Wed ..."
Abstract
-
Cited by 473 (6 self)
- Add to MetaCart
. Wedevelop a set of techniques for the rank aggregation problem and compare their performance to that of well-known methods. A primary goal of our work is to design rank aggregation techniques that can effectively combat "spam," a serious problem in Web searches. Experiments show that our methods
Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics
- J. Geophys. Res
, 1994
"... . A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The ..."
Abstract
-
Cited by 782 (22 self)
- Add to MetaCart
. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplified closure in the error covariance equation, is completely eliminated. Open boundaries can be handled as long as the ocean model is well posed. Well-known numerical instabilities associated with the error
Ensemble Methods in Machine Learning
- MULTIPLE CLASSIFIER SYSTEMS, LBCS-1857
, 2000
"... Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boostin ..."
Abstract
-
Cited by 607 (3 self)
- Add to MetaCart
Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging
On optimistic methods for concurrency control
- ACM Transactions on Database Systems
, 1981
"... Most current approaches to concurrency control in database systems rely on locking of data objects as a control mechanism. In this paper, two families of nonlocking concurrency controls are presented. The methods used are “optimistic ” in the sense that they rely mainly on transaction backup as a co ..."
Abstract
-
Cited by 547 (1 self)
- Add to MetaCart
Most current approaches to concurrency control in database systems rely on locking of data objects as a control mechanism. In this paper, two families of nonlocking concurrency controls are presented. The methods used are “optimistic ” in the sense that they rely mainly on transaction backup as a
Results 1 - 10
of
5,625,986
