Results 291 to 300 of about 2,291,078 (312)

Active Learning of Introductory Machine Learning

Proceedings. Frontiers in Education. 36th Annual Conference, 2006
This paper describes a computer-based training program for active learning of Agent Technology, Expert Systems, Neural Networks and Case-Based Reasoning by undergraduate students using a simple agent framework. While many Machine Learning (ML) and Artificial Intelligence (AI) courses teach ML and AI concepts by means of programming assignments, these ...
Maja Pantic, R. Zwitserloot
openaire   +2 more sources

Active Learning From Imbalanced Data: A Solution of Online Weighted Extreme Learning Machine

IEEE Transactions on Neural Networks and Learning Systems, 2019
It is well known that active learning can simultaneously improve the quality of the classification model and decrease the complexity of training instances. However, several previous studies have indicated that the performance of active learning is easily
Hualong Yu, Xibei Yang, Shang Zheng, Changyin Sun   +3 more
semanticscholar   +1 more source

Machine learning interatomic potential for silicon-nitride (Si3N4) by active learning

The Journal of Chemical Physics, 2023
Silicon nitride (Si3N4) is an extensively used material in the automotive, aerospace, and semiconductor industries. However, its widespread use is in contrast to the scarce availability of reliable interatomic potentials that can be employed to study various aspects of this material on an atomistic scale, particularly its amorphous phase. In this work,
Diego Milardovich, Christoph Wilhelmer, Dominic Waldhoer, Lukas Cvitkovich, Ganesh Sivaraman, Tibor Grasser   +5 more
openaire   +2 more sources

Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning.

ACS Macro Letters, 2021
Equilibrium phase diagrams serve as blueprints for rational design of nanostructured materials of block copolymers, but their construction is time-consuming and requires profound expertise.
Shuochen Zhao, Tianyu Cai, Liangshun Zhang, Weihua Li, Jiaping Lin   +4 more
semanticscholar   +1 more source

Interactive Machine Learning for Data Exfiltration Detection: Active Learning with Human Expertise

IEEE International Conference on Systems, Man and Cybernetics, 2020
Data exfiltration is a serious threat to organizations. Such exfiltrations cause breach events that can lead to millions of dollars of loss. Perimeter defense is not enough by itself since successful exploits from insiders can also be very damaging ...
Mu-Huan Chung, M. Chignell, Lu Wang, Alexandra Jovicic, Abhay Raman   +4 more
semanticscholar   +1 more source

Active Machine Learning for Consideration Heuristics

Marketing Science, 2011
We develop and test an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a “configurator.” Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy).
Daria Dzyabura, John R. Hauser
openaire   +2 more sources

A Machine Learning Approach for Human Activity Recognition

2020
Human Activity Recognition (HAR) is becoming a significant issue in modern times and directly impact the field of mobile health. Therefore, it is essential the designing of systems which are capable of recognizing properly the activities conducted by the individuals.
Petros Karvelis, Giannis Botilias, Angelos Papoutsis, Chrysostomos D. Stylios   +3 more
openaire   +2 more sources

Activation-Kernel Extraction through Machine Learning

2017 New Generation of CAS (NGCAS), 2017
Machine Learning flooded many research fields, including Electronic Design Automation (EDA). The availability of algorithms that can solve complex problems through generic rule formulations represent a fresh opportunity to improve existing design paradigms. In this work we investigate the use of machine learning to manipulate logic circuits.
Tenace, Valerio, Calimera, Andrea
openaire   +2 more sources

A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery

International Journal of Remote Sensing, 2020
Water-quality monitoring for small urban waterbodies by remote sensing gets to be difficult due to the coarse spatial resolution of remote-sensing imagery.
Hongwei Guo, J. Huang, Bowen Chen, Xiaolong Guo, V. Singh   +4 more
semanticscholar   +1 more source

An adaptive extreme learning machine based on an active learning method for structural reliability analysis

Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2021
Jiaming Cheng, Hui Jin
semanticscholar   +1 more source