A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
A Framework for Algorithm Stability
[article]
2018
arXiv
pre-print
Preserved Fulltext
Other Versions
In this paper we present a framework for analyzing the stability of algorithms. ...
Our framework allows for three types of stability analysis with increasing degrees of complexity: event stability, topological stability, and Lipschitz stability. ...
We present a framework to measure and analyze the stability of algorithms. ...
arXiv:1704.08000v2
fatcat:3iuaqeswyfbdtk2czx5nr4g2dm
SelfWISE: A Framework for Developing Self-Stabilizing Algorithms
[chapter]
2009
Kommunikation in Verteilten Systemen (KiVS)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Algorithms defined for abstract models Simple set operations Christoph Weyer Framework for Developing Self-Stabilizing Algorithms Christoph Weyer Framework for Developing Self-Stabilizing Algorithms 11 ...
Christoph Weyer
Framework for Developing Self-Stabilizing Algorithms
Christoph Weyer
Framework for Developing Self-Stabilizing Algorithms
8
8
Need for simplifying the programming
Hide low-level ...
SelfWISE: A Framework for Developing Self-Stabilizing Algorithms ...
doi:10.1007/978-3-540-92666-5_6
dblp:conf/kivs/WeyerT09
fatcat:wrrtwm7s5bh6npf6ouqornrd4m
A Framework for Algorithm Stability and Its Application to Kinetic Euclidean MSTs
[chapter]
2018
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
We present a framework to analyze the stability of (combinatorial) algorithms. ...
Conclusion We presented a framework for algorithm stability, which includes three types of stability analysis, namely event stability, topological stability, and Lipschitz stability. ...
doi:10.1007/978-3-319-77404-6_58
fatcat:ppdsa32kynhnhfxwbqi7ypjif4
The Stability and Accuracy Tradeoff Under Dataset Shift: A Causal Graphical Analysis
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
The hierarchy provides a common theoretical underpinning for understanding when and how stability to shifts can be achieved, and in what ways stable distributions can differ. ...
However, these methods consider different types of shifts and have been developed under disparate frameworks, making it difficult to theoretically analyze how solutions differ with respect to stability ...
Contributions In this paper, we provide a unifying framework for specifying dataset shifts that can occur, analyzing model stability to these shifts, and determining conditions for achieving the lowest ...
arXiv:1905.11374v4
fatcat:4zbmiwwekjchddoz67xl5p5s6a
A variance reduction framework for stable feature selection
2012
Statistical analysis and data mining
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
The file type is application/pdf.
The framework also suggests a variance reduction approach for improving the stability of feature selection algorithms. ...
In this study, we present a theoretical framework about the relationship between the stability and accuracy of feature selection based on a formal bias-variance decomposition of feature selection error ...
In addition, our study proposes an instance weighting framework for improving the stability of feature selection algorithms. ...
doi:10.1002/sam.11152
fatcat:duzdmtipjvbbbjo7wqq4b5auza
A Variance Reduction Framework for Stable Feature Selection
2010
2010 IEEE International Conference on Data Mining
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
The file type is application/pdf.
The framework also suggests a variance reduction approach for improving the stability of feature selection algorithms. ...
In this study, we present a theoretical framework about the relationship between the stability and accuracy of feature selection based on a formal bias-variance decomposition of feature selection error ...
In addition, our study proposes an instance weighting framework for improving the stability of feature selection algorithms. ...
doi:10.1109/icdm.2010.144
dblp:conf/icdm/HanY10
fatcat:3zipero2f5dlxgjjpjvlzp5ojm
Impact of EPON DBA Components on Performance
2011
2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
We introduce a convenient notational framework for Dynamic Bandwidth Allocation (DBA) algorithms in Ethernet Passive Optical Networks (EPONs) that uses the three principal axes of grant scheduling framework ...
We find that the grant sizing has the strongest impact on the delay and the combined grant scheduling framework and policy have the strongest impact on the stability limit. ...
CONCLUSION In conclusion, we presented a novel notational framework for identifying DBA algorithms and have conducted a performance evaluation that varies all three components of a DBA algorithm. ...
doi:10.1109/icccn.2011.6005773
dblp:conf/icccn/McGarryRAS11
fatcat:qhwni2iqarc2db7stacijekp2e
Self-Controllable Super-Resolution Deep Learning Framework for Surveillance Drones in Security Applications
2020
EAI Endorsed Transactions on Security and Safety
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
This paper proposes a self-controllable super-resolution adaptation algorithm in drone platforms. The drone platforms are generally used for surveillance in target network areas. ...
Thus, super-resolution algorithms which are for enhancing surveillance video quality are essential. ...
[19] design a secure learning framework for CCTVbased surveillance applications. ...
doi:10.4108/eai.30-6-2020.165502
fatcat:6hfw4eyta5fujac2op5ouilohy
Building Self-stabilizing Overlay Networks with the Transitive Closure Framework
[chapter]
2011
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
The goal in these papers is to design self-stabilizing algorithms for overlay network construction; these algorithms run on the individual nodes of a weakly-connected network and, by node actions that ...
We demonstrate the power of our framework by deriving from TCF a simple self-stabilizing protocol for constructing Skip+ graphs (Jacob et al., PODC 2009) which presents optimal convergence time from any ...
This is a "framework" rather than an algorithm and by instantiating certain subroutines in this framework we can obtain self-stabilizing algorithms for specific overlay networks. ...
doi:10.1007/978-3-642-24550-3_7
fatcat:34oi5wnxojgntdwthrqls2prdi
Building self-stabilizing overlay networks with the transitive closure framework
2013
Theoretical Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
The goal in these papers is to design self-stabilizing algorithms for overlay network construction; these algorithms run on the individual nodes of a weakly-connected network and, by node actions that ...
We demonstrate the power of our framework by deriving from TCF a simple self-stabilizing protocol for constructing Skip+ graphs (Jacob et al., PODC 2009) which presents optimal convergence time from any ...
This is a "framework" rather than an algorithm and by instantiating certain subroutines in this framework we can obtain self-stabilizing algorithms for specific overlay networks. ...
doi:10.1016/j.tcs.2013.02.021
fatcat:mb2w6ywbkvdqdbfyfqrqxulxli
Consensus group stable feature selection
2009
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
We propose a novel framework for stable feature selection which first identifies consensus feature groups from subsampling of training samples, and then performs feature selection by treating each consensus ...
In this paper, we show that stability of feature selection has a strong dependency on sample size. ...
ACKNOWLEDGMENTS The authors would like to thank anonymous reviewers for their helpful comments. C. Ding is partially supported by NSF-CCF-0830780 and NSF-DMS-0844497. ...
doi:10.1145/1557019.1557084
dblp:conf/kdd/LoscalzoYD09
fatcat:p6phltvaurf5hffpwj7w644nn4
Stable Feature Selection with Minimal Independent Dominating Sets
2007
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics - BCB'13
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Furthermore, the proposed MIDS framework complements standard feature selection algorithms like SVM-RFE, stability lasso and ensemble SVM RFE. ...
The main contribution is a novel framework for selecting most informative features which can preserve the linear combination property of the original feature space. ...
The proposed MIDS framework with the other two stability feature selection algorithms also has a higher stability on average. ...
doi:10.1145/2506583.2506600
dblp:conf/bcb/ShuML13
fatcat:5vfgxproqnhpdkbuhigs25hjtm
Programming Wireless Sensor Networks in a Self-Stabilizing Style
2009
2009 Third International Conference on Sensor Technologies and Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
The analysis is accomplished with Self-WISE, a framework providing programming abstractions for self-stabilizing algorithms. ...
This demonstrates the usability of SelfWISE for evaluating self-stabilizing algorithms under a wide range of models. ...
It consists of the SelfWISE framework (see Fig. 2 ) that is the runtime environment for executing self-stabilizing algorithm and a language to express those algorithms (see Fig. 1, reffig:color, and ...
doi:10.1109/sensorcomm.2009.100
fatcat:5eojslgr7rc23eeehhizlmvkgy
Margin Based Sample Weighting for Stable Feature Selection
[chapter]
2010
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
The file type is application/pdf.
A key factor affecting the stability of a feature selection algorithm is the sample size of training set. ...
We also develop an efficient algorithm under the framework. ...
To improve the stability of a feature selection
Algorithm 2. ...
doi:10.1007/978-3-642-14246-8_65
fatcat:lfakjzm6djeixifv6wzhxjsrbq
Fast Stability Scanning for Future Grid Scenario Analysis
[article]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a planning framework for fast stability scanning of future grid scenarios using a novel feature selection algorithm and a novel self-adaptive PSO-k-means clustering algorithm. ...
As a case study, we perform small-signal stability and steady-state voltage stability scanning of a simplified model of the Australian National Electricity Market with significant penetration of renewable ...
We propose a framework for fast stability scanning to achieve a significant computational speed-up. ...
arXiv:1701.03436v1
fatcat:6dopos5ab5g57kh5dr7hryc3c4
« Previous
Showing results 1 — 15 out of 411,297 results