Results 171 to 180 of about 1,909,109 (209)

Multi-objective spatial optimization of forest fire monitoring networks: An integrated GIS-MCDM framework enhanced by improved genetic algorithms. [PDF]

PLoS One
Wang L, Feng J, Mao J, Zhang Y, An J.
europepmc   +1 more source

Combining greedy and evolutionary algorithms to maximize influence in networks under deterministic linear threshold model. [PDF]

PLoS One
Andreev A, Kochemazov S, Semenov A.
europepmc   +1 more source

AutoXAI: a meta-learning approach for recommendation of explanation techniques. [PDF]

Sci Rep
Shawi RE, Jamel L.
europepmc   +1 more source

CO₂ Isotopologue Quantification Using Direct Frequency Comb Spectroscopy and Machine Learning. [PDF]

ACS Omega
Cochrane M, Scholten SK, Perrella C, van den Hengel A, Dholakia K, Luiten AN, Liao Z, Verjans JW.   +7 more
europepmc   +1 more source

Flexible Reconfigurations of Brain Networks During Decisions With Predefined Versus Self-Generated Options. [PDF]

Hum Brain Mapp
Wu Q, Zhang Z, Hsu M, Kayser AS.
europepmc   +1 more source

Submodular Optimization: Variants, Theory and Applications

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
openaire   +2 more sources

Multiobject Tracking by Submodular Optimization

IEEE Transactions on Cybernetics, 2019
© 2013 IEEE. In this paper, we propose a new multiobject visual tracking algorithm by submodular optimization. The proposed algorithm is composed of two main stages.
Dacheng Tao, Hanqiu Sun, Jianbing Shen
exaly   +5 more sources

Submodular Functions: Learnability, Structure, and Optimization [PDF]

SIAM Journal on Computing, 2018
Submodular functions are discrete functions that model laws of diminishing returns and enjoy numerous algorithmic applications. They have been used in many areas, including combinatorial optimization, machine learning, and economics. In this work we study submodular functions from a learning theoretic angle.
Maria-Florina Balcan
exaly   +3 more sources

Learning With Submodular Functions: A Convex Optimization Perspective

Foundations and Trends in Machine Learning, 2013
International audienceSubmodular functions are relevant to machine learning for at least two reasons: (1) some problems may be expressed directly as the optimization of submodular functions and (2) the lovasz extension of submodular functions provides a ...
Bach, Francis, Francis Bach
exaly   +2 more sources