IEEE Organizations related to Decision Algorithim

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Conferences related to Decision Algorithim

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2012 IEEE Conference on Prognostics and Health Management (PHM)

PHM 2012 is intended for researchers, R&D engineers and managers working in the expanding field of PHM. The primary objective is to foster collaboration and communication between academic, government, and industry PHM communities across the globe. The conference will cover a broad range of research and application topics related to PHM.

  • 2011 IEEE Conference on Prognostics and Health Management (PHM)

    PHM11 is intended for researchers, R&D engineers and managers working in the expanding field of PHM. The primary objective is to foster collaboration and communication between academic, government, and industry PHM communities across the globe. The conference will cover a broad range of research and application topics related to PHM.

  • 2008 IEEE International Conference on Prognostics and Health Management (PHM)

    PHM 08 is intended for researchers, R&D engineers and managers working in the expanding field of PHM. The primary objective is to foster collaboration and communication between academic, government, and industry PHM communities across the globe. The conference will cover a broad range of research and application topics related to PHM.



Periodicals related to Decision Algorithim

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Reliability, IEEE Transactions on

Principles and practices of reliability, maintainability, and product liability pertaining to electrical and electronic equipment.



Most published Xplore authors for Decision Algorithim

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Xplore Articles related to Decision Algorithim

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Feature selection in meta learning framework

2014 Science and Information Conference, 2014

Feature selection is a key step in data mining. Unfortunately, there is no single feature selection method that is always the best and the data miner usually has to experiment with different methods using a trial and error approach, which can be time consuming and costly especially with very large datasets. Hence, this research aims to develop a meta learning ...



Educational Resources on Decision Algorithim

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IEEE.tv Videos

An Introduction to Computational Intelligence in Multi-Criteria Decision-Making: The Intersection of Search, Preference Tradeoff
Bayesian Perception & Decision from Theory to Real World Applications
Fuzzy and Soft Methods for Multi-Criteria Decision Making - Ronald R Yager - WCCI 2016
Algorithmic Decision Making: Impacts and Implications - IEEE Internet Initiative Webinar
Fusing Simultaneously Acquired EEG and fMRI to Infer Spatiotemporal Dynamics of Cognition in the Human Brain - IEEE Brain Workshop
WIE: Our Own Voices - Noel Schulz, Kansas State University
PGX Clinical Decision Support Implementation - Peter Hulick - IEEE EMBS at NIH, 2019
Social Implications: Perils & Promises of AI - IEEE AI & Ethics Summit 2016
IEEE Authoring Parts 1 and 2: Publishing Choices
Robotics History: Narratives and Networks Oral Histories: Max Mintz
Crisis or Opportunity?: The Economic Impact on Underrepresented Communities - IEEE WIE ILC 2020 Virtual Series
Recurrent Neural Networks for System Identification, Forecasting and Control
A Conversation with…Francesca Rossi: IEEE TechEthics
Landing in a Self-Flying Airplane. Ready for it? - Antonio Crespo
Some Thoughts on a Gap Between Theory and Practice of Evolutionary Algorithms - WCCI 2012
Sensing and Decision Making in Social Networks
Photo Verification Technology for Radiology Images - Srini Tridandapani - IEEE EMBS at NIH, 2019
Energy Efficiency of MRR-based BDD Circuits - Ozan Yakar - ICRC San Mateo, 2019
Diab and Frazier: Ethernet in the First Mile
Adam Seiver EMB Conference on Individualized Healthcare

IEEE-USA E-Books

  • Feature selection in meta learning framework

    Feature selection is a key step in data mining. Unfortunately, there is no single feature selection method that is always the best and the data miner usually has to experiment with different methods using a trial and error approach, which can be time consuming and costly especially with very large datasets. Hence, this research aims to develop a meta learning framework that is able to learn about which feature selection methods work best for a given data set. The framework involves obtaining the characteristics of the data and then running alternative feature selection methods to obtain their performance. The characteristics, methods used and their performance provide the examples which are used by a learner to induce the meta knowledge which can then be applied to predict future performance on unseen data sets. This framework is implemented in the Weka system and experiments with 26 data sets show good results.



Standards related to Decision Algorithim

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No standards are currently tagged "Decision Algorithim"


Jobs related to Decision Algorithim

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