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Selection: with tag no-free-lunch-theorem [9 articles] 


Has artificial intelligence become alchemy?

Science, Vol. 360, No. 6388. (04 May 2018), pp. 478-478,


Ali Rahimi, a researcher in artificial intelligence (AI) at Google in San Francisco, California, has charged that machine learning algorithms, in which computers learn through trial and error, have become a form of "alchemy." Researchers, he says, do not know why some algorithms work and others don't, nor do they have rigorous criteria for choosing one AI architecture over another. Now, in a paper presented on 30 April at the International Conference on Learning Representations in Vancouver, Canada, Rahimi and his ...


The lack of a priori distinctions between learning algorithms

Neural Computation, Vol. 8, No. 7. (1 October 1996), pp. 1341-1390,


This is the first of two papers that use off-training set (OTS) error to investigate the assumption-free relationship between learning algorithms. This first paper discusses the senses in which there are no a priori distinctions between learning algorithms. (The second paper discusses the senses in which there are such distinctions.) In this first paper it is shown, loosely speaking, that for any two algorithms A and B, there are “as many” targets (or priors over targets) for which A has lower ...


(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 26

(February 2014)
Keywords: inrmm-list-of-tags   nectaroscordum-siculum   nectria-coccinea   negative-emission   negative-emissions   negative-learning   negative-studies   neglecting-non-monetary-criteria   negotiation   neighbourhood-analysis   nematus-melanaspis   nematus-oligospilus   nemoral-climate   neocallitropsis-pancheri   neodiprion-sertifer   neofusicoccum-parvum   neogene   neonicotinoid   nepal   nephelium-lappaceum   nerium-oleander   nested-loops-and-conditional-structures   netherlands   network-representation-capability   networks   neural-networks   neuro-dynamic-programming   neuroterus-spp   new-forested-areas   new-species   new-zealand   niche-limits   niche-model   niche-modelling   niche-sourcing   nickel   nigeria   nitrogen   nitrogen-deposition   nitrogen-fixation   nitrogen-leaching   nitrogen-partitioning   no-analog-pattern   no-analogue   no-free-lunch-theorem   nolina-recurvata   non-array-oriented   non-equilibrium   non-linearity   non-semantic-software-errors   non-stationarity   non-wood-products   nonadditive-measures   nonideal-neurons   nonlinear-correlation   nonlinear-response-to-bioclimatic-predictors   nonmarket-impacts   nonsteady-flame-convection   north-africa   north-america   northern-europe   northern-hemisphere   norway   not-automatic-workflow   notation   notation-as-a-tool-of-thought   nothofagus-cunninghamii   nothofagus-glauca   nothofagus-nervosa   nothofagus-procera   nothofagus-pumilio   nothofagus-spp   notholithocarpus-densiflorus   nothotsuga-spp   nreap-2020   nuclear-disasters   numerical-analysis   numpy   nurse-species   nut-producing-plants   nutrient-gradient   nutrient-recommendations   nutrient-rich-soil   nutrients   nutritional-composition   nyssa-spp   nyssa-sylvatica   oak-decline   oak-hornbeam-forest   oak-shake   object-classification   object-detection   object-oriented-programming   occam-razor   occupancy-vs-detection   ocean-acidification   ocean-circulation   oceans   ochroma-pyramidale   oenothera-spp  


List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( ). ...


Ecological relevance of performance criteria for species distribution models

Ecological Modelling, Vol. 221, No. 16. (10 August 2010), pp. 1995-2002,


Species distribution models have often been developed based on ecological data. To develop reliable data-driven models, however, a sound model training and evaluation procedures are needed. A crucial step in these procedures is the assessment of the model performance, with as key component the applied performance criterion. Therefore, we reviewed seven performance criteria commonly applied in presence–absence modelling (the correctly classified instances, Kappa, sensitivity, specificity, the normalised mutual information statistic, the true skill statistic and the odds ratio) and analysed their ...


Sparse Algorithms Are Not Stable: A No-Free-Lunch Theorem

Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 34, No. 1. (January 2012), pp. 187-193,


We consider two desired properties of learning algorithms: *sparsity* and *algorithmic stability*. Both properties are believed to lead to good generalization ability. We show that these two properties are fundamentally at odds with each other: a sparse algorithm cannot be stable and vice versa. Thus, one has to trade off sparsity and stability in designing a learning algorithm. In particular, our general result implies that $\ell_1$-regularized regression (Lasso) cannot be stable, while $\ell_2$-regularized regression is known to have strong stability properties ...


Ensemble based systems in decision making

Circuits and Systems Magazine, IEEE, Vol. 6, No. 3. (2006), pp. 21-45,


In matters of great importance that have financial, medical, social, or other implications, we often seek a second opinion before making a decision, sometimes a third, and sometimes many more. In doing so, we weigh the individual opinions, and combine them through some thought process to reach a final decision that is presumably the most informed one. The process of consulting "several experts" before making a final decision is perhaps second nature to us; yet, the extensive benefits of such a ...


There Is a Free Lunch for Hyper-Heuristics, Genetic Programming and Computer Scientists

In Genetic Programming, Vol. 5481 (2009), pp. 195-207,


In this paper we prove that in some practical situations, there is a free lunch for hyper-heuristics, i.e., for search algorithms that search the space of solvers, searchers, meta-heuristics and heuristics for problems. This has consequences for the use of genetic programming as a method to discover new search algorithms and, more generally, problem solvers. Furthermore, it has also rather important philosophical consequences in relation to the efforts of computer scientists to discover useful novel search algorithms. ...


Remarks on a recent paper on the "no free lunch" theorems

Evolutionary Computation, IEEE Transactions on In Evolutionary Computation, IEEE Transactions on, Vol. 5, No. 3. (June 2001), pp. 295-296,


This note discusses the recent paper "Some technical remarks on the proof of the no free lunch theorem" by Koppen (2000). In that paper, some technical issues related to the formal proof of the no free lunch (NFL) theorem for search were given by Wolpert and Macready (1995, 1997). The present authors explore the issues raised in that paper including the presentation of a simpler version of the NFL proof in accord with a suggestion made explicitly by Koppen (2000) and ...


No free lunch theorems for optimization

Evolutionary Computation, IEEE Transactions on, Vol. 1, No. 1. (06 April 1997), pp. 67-82,


A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of “no free lunch” (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem. Applications of the NFL theorems to information-theoretic aspects of optimization ...

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Integrated Natural Resources Modelling and Management - Meta-information Database.

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Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
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Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.