22 resources related to Decision Algorithim
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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.
Principles and practices of reliability, maintainability, and product liability pertaining to electrical and electronic equipment.
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 ...
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
WIE: Our Own Voices - Noel Schulz, Kansas State University
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
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
Adam Seiver EMB Conference on Individualized Healthcare
Diab and Frazier: Ethernet in the First Mile
A Conversation with…Toby Walsh: IEEE TechEthics
Q&A with Sorel Reisman & Sheikh Iqbal Ahamed, Part 1: IEEE Big Data Podcast, Episode 12
Multi-Standard 5Gbps to 28.2Gbps Adaptive, Single Voltage SerDes Transceiver with Analog FIR and 2-Tap Unrolled DFE in 28nm CMOS: RFIC Interactive Forum 2017
Doing the Right Thing: Social Implications of Technology (Member Access)
Risto Miikkilainen - Multiagent Learning Through Neuroevolution
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.
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