An efficient primal-dual method for solving non-smooth machine learning problem

2022 ◽  
Vol 155 ◽  
pp. 111754
Author(s):  
S. Lyaqini ◽  
M. Nachaoui ◽  
A. Hadri
Keyword(s):  
Machine Learning ◽  
Dual Method ◽  
Learning Problem ◽  
Primal Dual

scholarly journals APPROXIMATING SMALLEST ENCLOSING BALLS WITH APPLICATIONS TO MACHINE LEARNING

2009 ◽  
Vol 19 (05) ◽  
pp. 389-414 ◽  
Author(s):  
FRANK NIELSEN ◽  
RICHARD NOCK
Keyword(s):  
Machine Learning ◽  
Hybrid Algorithms ◽  
Decision Problems ◽  
Dual Method ◽  
Euclidean Ball ◽  
Prior Work ◽  
Euclidean Spaces ◽  
Information Theoretic ◽  
Primal Dual ◽  
Covering Set

In this paper, we first survey prior work for computing exactly or approximately the smallest enclosing balls of point or ball sets in Euclidean spaces. We classify previous work into three categories: (1) purely combinatorial, (2) purely numerical, and (3) recent mixed hybrid algorithms based on coresets. We then describe two novel tailored algorithms for computing arbitrary close approximations of the smallest enclosing Euclidean ball. These deterministic heuristics are based on solving relaxed decision problems using a primal-dual method. The primal-dual method is interpreted geometrically as solving for a minimum covering set, or dually as seeking for a minimum piercing set. Finally, we present some applications in machine learning of the exact and approximate smallest enclosing ball procedure, and discuss about its extension to non-Euclidean information-theoretic spaces.

Primal-dual method for continuous max-flow approaches

2015 ◽  
pp. 17-24 ◽  
Author(s):  
Ke Wei ◽  
Xue-Cheng Tai ◽  
Tony Chan ◽  
Shingyu Leung
Keyword(s):  
Dual Method ◽  
Primal Dual

scholarly journals Temporal Uncertainty During Overshadowing

2011 ◽  
pp. 46-55
Author(s):  
Dómhnall J. Jennings ◽  
Eduardo Alonso ◽  
Esther Mondragón ◽  
Charlotte Bonardi
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Associative Learning ◽  
Learning Theories ◽  
Temporal Difference ◽  
Temporal Uncertainty ◽  
Learning Problem ◽  
Difference Model ◽  
Distribution Form ◽  
Temporal Properties

Standard associative learning theories typically fail to conceptualise the temporal properties of a stimulus, and hence cannot easily make predictions about the effects such properties might have on the magnitude of conditioning phenomena. Despite this, in intuitive terms we might expect that the temporal properties of a stimulus that is paired with some outcome to be important. In particular, there is no previous research addressing the way that fixed or variable duration stimuli can affect overshadowing. In this chapter we report results which show that the degree of overshadowing depends on the distribution form - fixed or variable - of the overshadowing stimulus, and argue that conditioning is weaker under conditions of temporal uncertainty. These results are discussed in terms of models of conditioning and timing. We conclude that the temporal difference model, which has been extensively applied to the reinforcement learning problem in machine learning, accounts for the key findings of our study.

Can user models be learned at all? Inherent problems in machine learning for user modelling

2004 ◽  
Vol 19 (1) ◽  
pp. 61-88 ◽  
Author(s):  
MARTIN E. MÜLLER
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
User Model ◽  
User Modelling ◽  
Machine Learning Algorithms ◽  
Learning Problem ◽  
User Models ◽  
Modelling Techniques ◽  
Modelling Problem

Machine learning seems to offer the solution to many problems in user modelling. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions to machine learning. This article closely relates the user modelling problem to the machine learning problem. It explicates some inherent dilemmas that are likely to be overlooked when applying machine learning algorithms in user modelling. Some examples illustrate how specific approaches deliver satisfying results and discuss underlying assumptions on the domain or how learned hypotheses relate to the requirements on the user model. Finally, some new or underestimated approaches offering promising perspectives in combined systems are discussed. The article concludes with a tentative ‘‘checklist” that one might like to consider when planning to apply machine learning to user modelling techniques.

scholarly journals Accelerated dual-averaging primal–dual method for composite convex minimization

2020 ◽  
Vol 35 (4) ◽  
pp. 741-766
Author(s):  
Conghui Tan ◽  
Yuqiu Qian ◽  
Shiqian Ma ◽  
Tong Zhang
Keyword(s):  
Convex Minimization ◽  
Dual Method ◽  
Primal Dual ◽  
Dual Averaging
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