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Characterizing rational versus exponential learning curves
[chapter]

1995
*
Lecture Notes in Computer Science
*

By addressing a simple non-uniformity in the original analysis, this paper shows how the dichotomy between

doi:10.1007/3-540-59119-2_184
fatcat:tnxkpqkdvnbtjbf2kfwtcqcc6e
*rational*and*exponential*worst case*learning**curves*can be recovered in the distribution free ... Here a*learning**curve*can be de ned to be the expected error of a learner's hypotheses as a function of training sample size. ... Comparing uniform*versus*non-uniform bounds. This demonstrates how a series of*exponential**learning**curves*can have a*rational*upper envelope. ...##
###
Characterizing Rational versus Exponential Learning Curves

1997
*
Journal of computer and system sciences (Print)
*

By addressing a simple non-uniformity in the original analysis this paper shows how the dichotomy between

doi:10.1006/jcss.1997.1505
fatcat:twy72eqcrvhidcw3t3of733xha
*rational*and*exponential*worst case*learning**curves*can be recovered in the distribution-free theory ... We consider the standard problem of*learning*a concept from random examples. Here a*learning**curve*is defined to be the expected error of a learner's hypotheses as a function of training sample size. ... Of course, a necessary prerequisite for any practical*characterization*of empirical*learning**curves*is predicting whether*rational**versus**exponential*convergence will take place: obviously one cannot accurately ...##
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Page 2592 of Mathematical Reviews Vol. , Issue 97D
[page]

1997
*
Mathematical Reviews
*

Sanjay Jain (SGP-SING-IS; Singapore)
97d:68192 68T05
Schuurmans, Dale (3-TRNT-C; Toronto, ON)

*Characterizing**rational**versus**exponential**learning**curves*. ... “By addressing a simple non-uniformity in the original anal- ysis, this paper shows how the dichotomy between*rational*and*exponential*worst case*learning**curves*can be recovered in the distribution free ...##
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Page 2073 of Mathematical Reviews Vol. , Issue 99c
[page]

1991
*
Mathematical Reviews
*

*rational*

*versus*

*exponential*

*learning*

*curves*. ... Summary: “We define distances between geometric

*curves*by the square root of the minimal energy required to transform one

*curve*into the other. ...

##
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Page 1901 of Mathematical Reviews Vol. , Issue 97C
[page]

1997
*
Mathematical Reviews
*

examples (252-260); Eric Martin and Daniel Osherson, A note on the use of probabilities by mechanical learners (261-271); Dale Schuur- mans,

*Characterizing**rational**versus**exponential**learning**curves*... Schapire, A decision-theoretic generalization of on-line*learning*and an application to boosting (23-37); Shai Ben-David, Eyal Kushilevitz and Yishay Mansour, Online*learning**versus*offline*learning*(38 ...##
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Competitive Centipede Games: Zero-End Payoffs and Payoff Inequality Deter Reciprocal Cooperation

2015
*
Games
*

We investigated cooperation in four Centipede games differing in their payoffs at the game's end (positive

doi:10.3390/g6030262
fatcat:raykxt2dfrhnhogjtkyzlqxeie
*versus*zero) and payoff difference between players (moderate*versus*high difference). ... The*learning**curves*for the different games do not show any discernible trends of either increasing or decreasing cooperation with greater experience in the game. ... In a complex research design investigating decision making in Take-it-or-leave-it Centipede games with partially unknown payoff functions, continuous*versus*discrete moves, and simultaneous*versus*sequential ...##
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Some Thoughts on Reliability of Diagnoses by Human Versus by Machine

2020
*
Global Journal of Engineering Sciences
*

It means that with the elapsed time the standard deviation

doi:10.33552/gjes.2020.05.000611
fatcat:mfizkgbldzg2zpqlwoynwhnn6y
*characterizing*of the*learning*process of a given A.I. will decrease during the*learning*period. ... Accidental errors (others than the mentioned in the earlier groups) are*characterized*by Gaussian distribution [3] [4] [5] [6] . ... It will be interesting to see in the long run if the size of the data set used for machine*learning*-analogously to the afore mentioned laws -will follow an*exponential**curve*as a result of technological ...##
###
Simple Formulae, Deep Learning and Elaborate Modelling for the COVID-19 Pandemic

2022
*
Encyclopedia
*

It is emphasized that researchers' forecasting models exhibit, for large t, algebraic behavior, as opposed to the

doi:10.3390/encyclopedia2020047
fatcat:vfkm4qsiprdmpl3g35sr5d5zsi
*exponential*behavior of the classical logistic-type models used usually in epidemics. ... Remarkably, a newly introduced mechanistic model also exhibits, for large t, algebraic behavior in contrast to the usual Susceptible-Exposed-Infectious-Removed (SEIR) models, which exhibit*exponential*... by an algebraic as opposed to an*exponential*decay. ...##
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Beliefs, Doubts and Learning: Valuing Macroeconomic Risk

2007
*
The American Economic Review
*

The −

doi:10.1257/aer.97.2.1
fatcat:dzld3755mrdghaylsb5tyrgkny
*curve*was computed assuming that θ b = 24, and --*curve*was computed assuming that θ b = 6. ... The smooth*curves*in Figure 5 are computed for θ b = 24 and the more volatile*curves*for θ b = 6. ...##
###
Time scales in motor learning and development

2001
*
Psychological review
*

recognition of the importance of

doi:10.1037/0033-295x.108.1.57
pmid:11212633
fatcat:3xpza7o6gndihotkdbi7co6ori
*rational*or theoretical*curve*fitting*versus*that based on a mere empirical agenda (Guildford, 1936; Thurstone, 1919) . ... nature of the functions of motor*learning*rather than a narrowing of the*learning*problem to a theoretical*rationale*for a single function. ... We denote by n the practice time (number of trials) and by x n a variable that*characterizes*the movement pattern, that is, the associated performance. ...##
###
Time scales in motor learning and development

2001
*
Psychological review
*

recognition of the importance of

doi:10.1037//0033-295x.108.1.57
fatcat:5qngkzxm3ngh5ndfbzmg7y3mou
*rational*or theoretical*curve*fitting*versus*that based on a mere empirical agenda (Guildford, 1936; Thurstone, 1919) . ... nature of the functions of motor*learning*rather than a narrowing of the*learning*problem to a theoretical*rationale*for a single function. ... We denote by n the practice time (number of trials) and by x n a variable that*characterizes*the movement pattern, that is, the associated performance. ...##
###
Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2

2020
*
Journal of the Royal Society Interface
*

This methodology, which is based on the synergy of explicit mathematical formulae and deep

doi:10.1098/rsif.2020.0494
pmid:32752997
fatcat:vt75pi2b2ndl3on4h7zf7s2dba
*learning*networks, yields algorithms whose input is only the existing data in the given country of the accumulative ... Figure 4 presents the predictions made by the analytical formulae and the deep*learning*network*versus*the actual data for the cumulative number of reported cases due to SARS-CoV-2, as a function of days ... Hence, choosing these parameters by requiring that the analytical solution matches the data*curve*is consistent with the approach of machine*learning*. ...##
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Stabilization Theory and Policy: 50 Years after the Phillips Curve

2009
*
Economica
*

The

doi:10.1111/j.1468-0335.2009.00807.x
fatcat:47piiah5anco5mmuewoki3myke
*learning*procedures we have been outlining can be*characterized*as being "passive", in the sense that the agent*learns*about the relevant parameters over time as the system evolves and information ... Fourth,*learning*may take different forms, the two most common being least squares*learning*and Bayesian*learning*. ...##
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An Adversarial Interpretation of Information-Theoretic Bounded Rationality
[article]

2014
*
arXiv
*
pre-print

The adversary can, by paying an

arXiv:1404.5668v1
fatcat:tqhmsfibb5edzcsh2tqtijkvlq
*exponential*penalty, generate costs that diminish the decision maker's payoffs. ... IT bounded*rationality*addresses this question by defining a new objective function that trades off utilities*versus*information costs. ... Given a function f (x), the convex conjugate f (s) corresponds to the intercept of a tangent line to the*curve*with slope s. 4. ...##
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An Adversarial Interpretation of Information-Theoretic Bounded Rationality

2014
*
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
*

The adversary can, by paying an

doi:10.1609/aaai.v28i1.9071
fatcat:yn3pw2ko5vddtowxq7rnh6psp4
*exponential*penalty, generate costs that diminish the decision maker's payoffs. ... IT bounded*rationality*addresses this question by defining a new objective function that trades off utilities*versus*information costs. ... Given a function f (x), the convex conjugate f (s) corresponds to the intercept of a tangent line to the*curve*with slope s. 4. ...
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