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Inmachine learning, thekernel perceptronis a variant of the popularperceptronlearning algorithm that can learnkernel machines, i.e. non-linearclassifiersthat employ a kernel function to compute the similarity of unseen samples to training samples. The algorithm was invented in 1964,[1]making it the first kernel classif... | https://en.wikipedia.org/wiki/Kernel_perceptron |
Inmachine learning, theradial basis functionkernel, orRBF kernel, is a popularkernel functionused in variouskernelizedlearning algorithms. In particular, it is commonly used insupport vector machineclassification.[1]
The RBF kernel on two samplesx∈Rk{\displaystyle \mathbf {x} \in \mathbb {R} ^{k}}andx′{\displaystyle \... | https://en.wikipedia.org/wiki/Radial_basis_function_kernel |
In situadaptive tabulation(ISAT) is analgorithmfor the approximation ofnonlinearrelationships. ISAT is based onmultiple linear regressionsthat are dynamically added as additional information is discovered. The technique is adaptive as it adds new linear regressions dynamically to a store of possible retrieval points.... | https://en.wikipedia.org/wiki/In_Situ_Adaptive_Tabulation |
Chaos theoryis aninterdisciplinaryarea ofscientific studyand branch ofmathematics. It focuses on underlying patterns anddeterministiclawsofdynamical systemsthat are highly sensitive toinitial conditions. These were once thought to have completely random states of disorder and irregularities.[1]Chaos theory states that ... | https://en.wikipedia.org/wiki/Chaos_theory |
Incomputer graphics,hierarchical RBFis aninterpolationmethod based onradial basis functions(RBFs). Hierarchical RBF interpolation has applications in treatment of results from a3D scanner,terrainreconstruction, and the construction of shape models in3D computer graphics(such as theStanford bunny, a popular 3D model).
... | https://en.wikipedia.org/wiki/Hierarchical_RBF |
Instantaneously trained neural networksarefeedforward artificial neural networksthat create a new hidden neuron node for each novel training sample. The weights to this hidden neuron separate out not only this training sample but others that are near it, thus providing generalization.[1][2]This separation is done using... | https://en.wikipedia.org/wiki/Instantaneously_trained_neural_networks |
Inartificial neural networks, ahybrid Kohonen self-organizing mapis a type ofself-organizing map(SOM) named for theFinnishprofessorTeuvo Kohonen, where the network architecture consists of an input layer fully connected to a 2–D SOM or Kohonen layer.
The output from the Kohonen layer, which is the winning neuron, feed... | https://en.wikipedia.org/wiki/Hybrid_Kohonen_self-organizing_map |
Incomputer science,learning vector quantization(LVQ) is aprototype-basedsupervisedclassificationalgorithm. LVQ is the supervised counterpart ofvector quantizationsystems.
LVQ can be understood as a special case of anartificial neural network, more precisely, it applies awinner-take-allHebbian learning-based approach. ... | https://en.wikipedia.org/wiki/Learning_vector_quantization |
Inmathematics, more specifically innumerical linear algebra, thebiconjugate gradient methodis analgorithmto solvesystems of linear equations
Unlike theconjugate gradient method, this algorithm does not require thematrixA{\displaystyle A}to beself-adjoint, but instead one needs to perform multiplications by theconjugat... | https://en.wikipedia.org/wiki/Biconjugate_gradient_method |
Innumerical linear algebra, theconjugate gradient squared method (CGS)is aniterativealgorithm for solvingsystems of linear equationsof the formAx=b{\displaystyle A{\mathbf {x}}={\mathbf {b}}}, particularly in cases where computing thetransposeAT{\displaystyle A^{T}}is impractical.[1]The CGS method was developed as an i... | https://en.wikipedia.org/wiki/Conjugate_gradient_squared_method |
Theconjugate residual methodis an iterativenumeric methodused for solvingsystems of linear equations. It's aKrylov subspace methodvery similar to the much more popularconjugate gradient method, with similar construction and convergence properties.
This method is used to solve linear equations of the form
whereAis an ... | https://en.wikipedia.org/wiki/Conjugate_residual_method |
Belief propagation, also known assum–product message passing, is a message-passingalgorithmfor performinginferenceongraphical models, such asBayesian networksandMarkov random fields. It calculates themarginal distributionfor each unobserved node (or variable), conditional on any observed nodes (or variables). Belief pr... | https://en.wikipedia.org/wiki/Belief_propagation#Gaussian_belief_propagation_.28GaBP.29 |
Incomputational mathematics, aniterative methodis amathematical procedurethat uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which thei-th approximation (called an "iterate") is derived from the previous ones.
A specific implementation withterminationcriteri... | https://en.wikipedia.org/wiki/Iterative_method#Linear_systems |
Inlinear algebra, the order-rKrylov subspacegenerated by ann-by-nmatrixAand a vectorbof dimensionnis thelinear subspacespannedby theimagesofbunder the firstrpowers ofA(starting fromA0=I{\displaystyle A^{0}=I}), that is,[1][2]
The concept is named after Russian applied mathematician and naval engineerAlexei Krylov, who... | https://en.wikipedia.org/wiki/Krylov_subspace |
Innumerical optimization, thenonlinear conjugate gradient methodgeneralizes theconjugate gradient methodtononlinear optimization. For a quadratic functionf(x){\displaystyle \displaystyle f(x)}
the minimum off{\displaystyle f}is obtained when thegradientis 0:
Whereas linear conjugate gradient seeks a solution to the l... | https://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method |
Sparse matrix–vector multiplication(SpMV) of the formy=Axis a widely usedcomputational kernelexisting in many scientific applications. The input matrixAissparse. The input vectorxand the output vectoryare dense. In the case of a repeatedy=Axoperation involving the same input matrixAbut possibly changing numerical value... | https://en.wikipedia.org/wiki/Sparse_matrix%E2%80%93vector_multiplication |
TheRescorla–Wagner model("R-W") is a model ofclassical conditioning, in which learning is conceptualized in terms of associations between conditioned (CS) and unconditioned (US) stimuli. A strong CS-US association means that the CS signals predict the US. One might say that before conditioning, the subject is surprised... | https://en.wikipedia.org/wiki/Rescorla%E2%80%93Wagner_model |
TheBerndt–Hall–Hall–Hausman(BHHH)algorithmis anumerical optimizationalgorithmsimilar to theNewton–Raphson algorithm, but it replaces the observed negativeHessian matrixwith theouter productof thegradient. This approximation is based on theinformation matrixequality and therefore only valid while maximizing alikelihood ... | https://en.wikipedia.org/wiki/BHHH_algorithm |
Limited-memory BFGS(L-BFGSorLM-BFGS) is anoptimizationalgorithmin the family ofquasi-Newton methodsthat approximates theBroyden–Fletcher–Goldfarb–Shanno algorithm(BFGS) using a limited amount ofcomputer memory.[1]It is a popular algorithm for parameter estimation inmachine learning.[2][3]The algorithm's target problem ... | https://en.wikipedia.org/wiki/L-BFGS |
Pattern search(also known as direct search, derivative-free search, or black-box search) is a family of numericaloptimizationmethods that does not require agradient. As a result, it can be used on functions that are notcontinuousordifferentiable. One such pattern search method is "convergence" (see below), which is bas... | https://en.wikipedia.org/wiki/Pattern_search_(optimization) |
Innumerical analysis, aquasi-Newton methodis aniterative numerical methodused either tofind zeroesor tofind local maxima and minimaof functions via an iterativerecurrence formulamuch like the one forNewton's method, except using approximations of thederivativesof the functions in place of exact derivatives. Newton's me... | https://en.wikipedia.org/wiki/Quasi-Newton_methods |
TheSymmetric Rank 1(SR1) method is aquasi-Newton methodto update the second derivative (Hessian)
based on the derivatives (gradients) calculated at two points. It is a generalization to thesecant methodfor a multidimensional problem.
This update maintains thesymmetryof the matrix but doesnotguarantee that the update be... | https://en.wikipedia.org/wiki/Symmetric_rank-one |
Thecompact representationforquasi-Newton methodsis amatrix decomposition, which is typically used ingradientbasedoptimizationalgorithmsor for solvingnonlinear systems. The decomposition uses a low-rank representation for the direct and/or inverseHessianor theJacobianof a nonlinear system. Because of this, the compact r... | https://en.wikipedia.org/wiki/Compact_quasi-Newton_representation |
Innumerical analysis, theNewton–Raphson method, also known simply asNewton's method, named afterIsaac NewtonandJoseph Raphson, is aroot-finding algorithmwhich produces successively betterapproximationsto theroots(or zeroes) of areal-valuedfunction. The most basic version starts with areal-valued functionf, itsderivativ... | https://en.wikipedia.org/wiki/Newton%27s_method |
Innumerical analysis, aquasi-Newton methodis aniterative numerical methodused either tofind zeroesor tofind local maxima and minimaof functions via an iterativerecurrence formulamuch like the one forNewton's method, except using approximations of thederivativesof the functions in place of exact derivatives. Newton's me... | https://en.wikipedia.org/wiki/Quasi-Newton_method |
Limited-memory BFGS(L-BFGSorLM-BFGS) is anoptimizationalgorithmin the family ofquasi-Newton methodsthat approximates theBroyden–Fletcher–Goldfarb–Shanno algorithm(BFGS) using a limited amount ofcomputer memory.[1]It is a popular algorithm for parameter estimation inmachine learning.[2][3]The algorithm's target problem ... | https://en.wikipedia.org/wiki/Limited-memory_BFGS |
Derivative-free optimization(sometimes referred to asblackbox optimization) is a discipline inmathematical optimizationthat does not usederivativeinformation in the classical sense to find optimal solutions: Sometimes information about the derivative of the objective functionfis unavailable, unreliable or impractical t... | https://en.wikipedia.org/wiki/Derivative-free_optimization |
Michael James David PowellFRSFAA[2](29 July 1936 – 19 April 2015) was a British mathematician, who worked in theDepartment of Applied Mathematics and Theoretical Physics(DAMTP) at theUniversity of Cambridge.[3][1][4][5][6]
Born in London, Powell was educated atFrensham Heights SchoolandEastbourne College.[2]He earned ... | https://en.wikipedia.org/wiki/COBYLA |
Michael James David PowellFRSFAA[2](29 July 1936 – 19 April 2015) was a British mathematician, who worked in theDepartment of Applied Mathematics and Theoretical Physics(DAMTP) at theUniversity of Cambridge.[3][1][4][5][6]
Born in London, Powell was educated atFrensham Heights SchoolandEastbourne College.[2]He earned ... | https://en.wikipedia.org/wiki/NEWUOA |
Michael James David PowellFRSFAA[2](29 July 1936 – 19 April 2015) was a British mathematician, who worked in theDepartment of Applied Mathematics and Theoretical Physics(DAMTP) at theUniversity of Cambridge.[3][1][4][5][6]
Born in London, Powell was educated atFrensham Heights SchoolandEastbourne College.[2]He earned ... | https://en.wikipedia.org/wiki/LINCOA |
Innumericaloptimization, theBroyden–Fletcher–Goldfarb–Shanno(BFGS)algorithmis aniterative methodfor solving unconstrainednonlinear optimizationproblems.[1]Like the relatedDavidon–Fletcher–Powell method, BFGS determines thedescent directionbypreconditioningthegradientwith curvature information. It does so by gradually i... | https://en.wikipedia.org/wiki/BFGS_method |
Differential evolution(DE) is anevolutionary algorithmtooptimizea problem byiterativelytrying to improve acandidate solutionwith regard to a given measure of quality. Such methods are commonly known asmetaheuristicsas they make few or no assumptions about the optimized problem and can search very large spaces of candid... | https://en.wikipedia.org/wiki/Differential_evolution |
Covariance matrix adaptation evolution strategy (CMA-ES)is a particular kind of strategy fornumerical optimization.Evolution strategies(ES) arestochastic,derivative-free methodsfornumerical optimizationof non-linearor non-convexcontinuous optimizationproblems. They belong to the class ofevolutionary algorithmsandevolut... | https://en.wikipedia.org/wiki/CMA-ES |
Agreedy algorithmis anyalgorithmthat follows the problem-solvingheuristicof making the locally optimal choice at each stage.[1]In many problems, a greedy strategy does not produce an optimal solution, but a greedy heuristic can yield locally optimal solutions that approximate a globally optimal solution in a reasonable... | https://en.wikipedia.org/wiki/Greedy_algorithm |
AWalrasian auction, introduced byLéon Walras, is a type of simultaneousauctionwhere eachagentcalculates its demand for the good at every possible price and submits this to an auctioneer. The price is then set so that the total demand across all agents equals the total amount of the good. Thus, a Walrasian auction perfe... | https://en.wikipedia.org/wiki/Walrasian_auction |
Mean shiftis anon-parametricfeature-spacemathematical analysis technique for locating the maxima of adensity function, a so-calledmode-seeking algorithm.[1]Application domains includecluster analysisincomputer visionandimage processing.[2]
The mean shift procedure is usually credited to work by Fukunaga and Hostetler ... | https://en.wikipedia.org/wiki/Mean-shift |
NeuroEvolution of Augmenting Topologies(NEAT) is agenetic algorithm(GA) for generating evolvingartificial neural networks(aneuroevolutiontechnique) developed byKenneth StanleyandRisto Miikkulainenin 2002 while atThe University of Texas at Austin. It alters both the weighting parameters and structures of networks, attem... | https://en.wikipedia.org/wiki/NeuroEvolution_of_Augmenting_Topologies |
Hypercube-based NEAT, orHyperNEAT,[1]is a generative encoding that evolvesartificial neural networks(ANNs) with the principles of the widely usedNeuroEvolution of Augmented Topologies(NEAT) algorithm developed byKenneth Stanley.[2]It is a novel technique for evolving large-scale neural networks using the geometric regu... | https://en.wikipedia.org/wiki/HyperNEAT |
Evolutionary acquisition of neural topologies(EANT/EANT2) is anevolutionaryreinforcement learningmethod that evolves both the topology and weights ofartificial neural networks. It is closely related to the works of Angeline et al.[1]and Stanley and Miikkulainen.[2]Like the work of Angeline et al., the method uses a typ... | https://en.wikipedia.org/wiki/Evolutionary_Acquisition_of_Neural_Topologies |
Inmachine learning,grokking, ordelayed generalization, is a transition togeneralizationthat occurs many training iterations after theinterpolation threshold, after many iterations of seemingly little progress, as opposed to the usual process where generalization occurs slowly and progressively once the interpolation th... | https://en.wikipedia.org/wiki/Grokking_(machine_learning) |
Meta-optimizationfromnumerical optimizationis the use of one optimization method to tune another optimization method. Meta-optimization is reported to have been used as early as in the late 1970s by Mercer and Sampson[1]for finding optimal parameter settings of agenetic algorithm.
Meta-optimization and related concept... | https://en.wikipedia.org/wiki/Meta-optimization |
XGBoost[2](eXtreme Gradient Boosting) is anopen-sourcesoftware librarywhich provides aregularizinggradient boostingframework forC++,Java,Python,[3]R,[4]Julia,[5]Perl,[6]andScala. It works onLinux,Microsoft Windows,[7]andmacOS.[8]From the project description, it aims to provide a "Scalable, Portable and Distributed Grad... | https://en.wikipedia.org/wiki/XGBoost |
Analysis of variance (ANOVA)is a family ofstatistical methodsused to compare themeansof two or more groups by analyzing variance. Specifically, ANOVA compares the amount of variationbetweenthe group means to the amount of variationwithineach group. If the between-group variation is substantially larger than the within-... | https://en.wikipedia.org/wiki/Analysis_of_variance |
Instatistics, thecoefficient of determination, denotedR2orr2and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).
It is astatisticused in the context ofstatistical modelswhose main purpose is either thepredictionof future outcomes ... | https://en.wikipedia.org/wiki/R-squared |
Instatistics, thefraction of variance unexplained(FVU) in the context of aregression taskis the fraction of variance of theregressand(dependent variable)Ywhich cannot be explained, i.e., which is not correctly predicted, by theexplanatory variablesX.
Suppose we are given a regression functionf{\displaystyle f}yielding... | https://en.wikipedia.org/wiki/Fraction_of_variance_unexplained |
Inprobability theoryandstatistics,varianceis theexpected valueof thesquared deviation from the meanof arandom variable. Thestandard deviation(SD) is obtained as the square root of the variance. Variance is a measure ofdispersion, meaning it is a measure of how far a set of numbers is spread out from their average value... | https://en.wikipedia.org/wiki/Variance#Variance_decomposition |
Instatistics, theLehmann–Scheffé theoremis a prominent statement, tying together the ideas of completeness, sufficiency, uniqueness, and best unbiased estimation.[1]The theorem states that anyestimatorthat isunbiasedfor a given unknown quantity and that depends on the data only through acomplete,sufficient statisticis ... | https://en.wikipedia.org/wiki/Lehmann%E2%80%93Scheff%C3%A9_theorem |
Instatistical theory, aU-statisticis a class of statistics defined as the average over the application of a given function applied to all tuples of a fixed size. The letter "U" stands for unbiased.[citation needed]In elementary statistics, U-statistics arise naturally in producingminimum-variance unbiased estimators.
... | https://en.wikipedia.org/wiki/U-statistic |
Theanalysis of competing hypotheses(ACH) is amethodologyfor evaluating multiple competing hypotheses for observed data. It was developed byRichards (Dick) J. Heuer, Jr., a 45-year veteran of theCentral Intelligence Agency, in the 1970s for use by the Agency.[1]ACH is used by analysts in various fields who make judgment... | https://en.wikipedia.org/wiki/Analysis_of_competing_hypotheses |
Instatisticsandmachine learning, thebias–variance tradeoffdescribes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as we increase the number of tunable parameters in a model... | https://en.wikipedia.org/wiki/Bias-variance_dilemma |
Inmachine learning,hyperparameter optimization[1]or tuning is the problem of choosing a set of optimalhyperparametersfor a learning algorithm. A hyperparameter is aparameterwhose value is used to control the learning process, which must be configured before the process starts.[2][3]
Hyperparameter optimization determi... | https://en.wikipedia.org/wiki/Grid_search |
Identifiability analysisis a group of methods found inmathematical statisticsthat are used to determine how well the parameters of a model are estimated by the quantity and quality ofexperimental data.[1]Therefore, these methods explore not onlyidentifiabilityof a model, but also the relation of the model to particular... | https://en.wikipedia.org/wiki/Identifiability_Analysis |
Log-linear analysisis a technique used instatisticsto examine the relationship between more than twocategorical variables. The technique is used for bothhypothesis testingand model building. In both these uses, models are tested to find the most parsimonious (i.e., least complex) model that best accounts for the varian... | https://en.wikipedia.org/wiki/Log-linear_analysis |
Instatistics,identifiabilityis a property which amodelmust satisfy for preciseinferenceto be possible. A model isidentifiableif it is theoretically possible to learn the true values of this model's underlying parameters after obtaining an infinite number of observations from it. Mathematically, this is equivalent to sa... | https://en.wikipedia.org/wiki/Model_identification |
In thedesign of experiments,optimal experimental designs(oroptimum designs[2]) are a class ofexperimental designsthat areoptimalwith respect to somestatisticalcriterion. The creation of this field of statistics has been credited to Danish statisticianKirstine Smith.[3][4]
In thedesign of experimentsforestimatingstatis... | https://en.wikipedia.org/wiki/Optimal_design#Model_selection |
Ineconomicsandeconometrics, theparameter identification problemarises when the value of one or moreparametersin aneconomic modelcannot be determined from observable variables. It is closely related tonon-identifiabilityinstatisticsand econometrics, which occurs when astatistical modelhas more than one set of parameters... | https://en.wikipedia.org/wiki/Parameter_identification_problem |
Scientific modellingis an activity that producesmodelsrepresentingempiricalobjects, phenomena, and physical processes, to make a particular part or feature of the world easier tounderstand,define,quantify,visualize, orsimulate. It requires selecting and identifying relevant aspects of a situation in the real world and ... | https://en.wikipedia.org/wiki/Scientific_modelling |
Indecision theoryandestimation theory,Stein's example(also known asStein's phenomenonorStein's paradox) is the observation that when three or more parameters are estimated simultaneously, there exist combinedestimatorsmore accurate on average (that is, having lower expectedmean squared error) than any method that handl... | https://en.wikipedia.org/wiki/Stein%27s_example |
Instatisticsthemean squared prediction error(MSPE), also known asmean squared error of the predictions, of asmoothing,curve fitting, orregressionprocedure is theexpected valueof thesquaredprediction errors(PE), thesquare differencebetween the fitted values implied by the predictive functiong^{\displaystyle {\widehat {g... | https://en.wikipedia.org/wiki/Prediction_error |
Statistical conclusion validityis the degree to which conclusions about the relationship amongvariablesbased on the data are correct or "reasonable". This began as being solely about whether the statistical conclusion about the relationship of the variables was correct, but now there is a movement towards moving to "re... | https://en.wikipedia.org/wiki/Statistical_conclusion_validity |
Instatistics,model specificationis part of the process of building astatistical model: specification consists of selecting an appropriatefunctional formfor the model and choosing which variables to include. For example, givenpersonal incomey{\displaystyle y}together with years of schoolings{\displaystyle s}and on-the-j... | https://en.wikipedia.org/wiki/Statistical_model_specification |
Instatistics, thecoefficient of determination, denotedR2orr2and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).
It is astatisticused in the context ofstatistical modelswhose main purpose is either thepredictionof future outcomes ... | https://en.wikipedia.org/wiki/Coefficient_of_determination |
Instatistics, asum of squares due to lack of fit, or more tersely alack-of-fit sum of squares, is one of the components of a partition of thesum of squaresof residuals in ananalysis of variance, used in thenumeratorin anF-testof thenull hypothesisthat says that a proposed model fits well. The other component is thepure... | https://en.wikipedia.org/wiki/Lack-of-fit_sum_of_squares |
Instatistics, thereduced chi-square statisticis used extensively ingoodness of fittesting. It is also known asmean squared weighted deviation(MSWD) inisotopic dating[1]andvariance of unit weightin the context ofweighted least squares.[2][3]
Its square root is calledregression standard error,[4]standard error of the re... | https://en.wikipedia.org/wiki/Reduced_chi-squared |
Instatistics, theChapman–Robbins boundorHammersley–Chapman–Robbins boundis a lower bound on thevarianceofestimatorsof a deterministic parameter. It is a generalization of theCramér–Rao bound; compared to the Cramér–Rao bound, it is both tighter and applicable to a wider range of problems. However, it is usually more di... | https://en.wikipedia.org/wiki/Chapman%E2%80%93Robbins_bound |
Ininformation theoryandstatistics,Kullback's inequalityis a lower bound on theKullback–Leibler divergenceexpressed in terms of thelarge deviationsrate function.[1]IfPandQareprobability distributionson the real line, such thatPisabsolutely continuouswith respect toQ, i.e.P<<Q, and whose first moments exist, thenDKL(P∥Q)... | https://en.wikipedia.org/wiki/Kullback%27s_inequality |
Inmathematics, theBrascamp–Lieb inequalityis either of two inequalities. The first is a result ingeometryconcerningintegrable functionsonn-dimensionalEuclidean spaceRn{\displaystyle \mathbb {R} ^{n}}. It generalizes theLoomis–Whitney inequalityandHölder's inequality. The second is a result of probability theory which g... | https://en.wikipedia.org/wiki/Brascamp%E2%80%93Lieb_inequality |
Aprediction(Latinpræ-, "before," anddictum, "something said"[1]) orforecastis a statement about afutureeventor about futuredata. Predictions are often, but not always, based upon experience or knowledge of forecasters. There is no universal agreement about the exact difference between "prediction" and "estimation"; dif... | https://en.wikipedia.org/wiki/Prediction |
Aconfidence bandis used instatistical analysisto represent the uncertainty in an estimate of a curve or function based on limited or noisy data. Similarly, aprediction bandis used to represent the uncertainty about the value of a new data-point on the curve, but subject to noise. Confidence and prediction bands are oft... | https://en.wikipedia.org/wiki/Confidence_and_prediction_bands |
Seymour Geisser(October 5, 1929 – March 11, 2004) was an Americanstatisticiannoted for emphasizingpredictive inference. In his bookPredictive Inference: An Introduction, he held that conventional statistical inference about unobservable population parameters amounts to inference about things that do not exist, followin... | https://en.wikipedia.org/wiki/Seymour_Geisser |
Indecision theory, ascoring rule[1]provides evaluation metrics forprobabilistic predictions or forecasts. While "regular" loss functions (such asmean squared error) assign a goodness-of-fit score to a predicted value and an observed value, scoring rules assign such a score to a predicted probability distribution and an... | https://en.wikipedia.org/wiki/Scoring_function |
Risk assessmentdetermines possible mishaps, their likelihood and consequences, and thetolerancesfor such events.[1][2]The results of this process may be expressed in aquantitativeorqualitativefashion. Risk assessment is an inherent part of a broaderrisk managementstrategy to help reduce any potential risk-related conse... | https://en.wikipedia.org/wiki/Risk_assessment |
Ineconomicsandfinance,risk aversionis the tendency of people to prefer outcomes with lowuncertaintyto those outcomes with high uncertainty, even if the average outcome of the latter is equal to or higher in monetary value than the more certain outcome.[1]
Risk aversion explains the inclination to agree to a situation ... | https://en.wikipedia.org/wiki/Risk_aversion |
Incomputer programming,genetic representationis a way of presenting solutions/individuals inevolutionary computationmethods. The term encompasses both the concretedata structuresanddata typesused to realize the genetic material of the candidate solutions in the form of a genome, and the relationships between search spa... | https://en.wikipedia.org/wiki/Genetic_representation |
Afitness functionis a particular type ofobjective or cost functionthat is used to summarize, as a singlefigure of merit, how close a given candidate solution is to achieving the set aims. It is an important component ofevolutionary algorithms (EA), such asgenetic programming,evolution strategiesorgenetic algorithms. An... | https://en.wikipedia.org/wiki/Fitness_function |
Selectionis agenetic operatorin anevolutionary algorithm(EA). An EA is ametaheuristicinspired bybiological evolutionand aims to solve challenging problems at leastapproximately. Selection has a dual purpose: on the one hand, it can choose individual genomes from a population for subsequent breeding (e.g., using thecros... | https://en.wikipedia.org/wiki/Selection_(genetic_algorithm) |
AlphaZerois acomputer programdeveloped byartificial intelligenceresearch companyDeepMindto master the games ofchess,shogiandgo. Thisalgorithmuses an approach similar toAlphaGo Zero.
On December 5, 2017, the DeepMind team released apreprintpaper introducing AlphaZero,[1]which would soon play three games by defeating wo... | https://en.wikipedia.org/wiki/AlphaZero |
MuZerois acomputer programdeveloped byartificial intelligenceresearch companyDeepMindto master games without knowing their rules.[1][2][3]Its release in 2019 included benchmarks of its performance ingo,chess,shogi, and a standard suite ofAtarigames. Thealgorithmuses an approach similar toAlphaZero. It matched AlphaZero... | https://en.wikipedia.org/wiki/MuZero |
Invideo games,artificial intelligence(AI) is used to generate responsive, adaptive orintelligentbehaviors primarily innon-playable characters(NPCs) similar tohuman-like intelligence. Artificial intelligence has been an integral part of video games since their inception in 1948, first seen in the gameNim.[1]AI in video ... | https://en.wikipedia.org/wiki/Artificial_intelligence_in_video_games |
Game Description Language(GDL) is a specializedlogicprogramming languagedesigned byMichael Genesereth. The goal of GDL is to allow the development of AI agents capable ofgeneral game playing. It is part of the General Game Playing Project atStanford University.
GDL is a tool for expressing the intricacies of game rule... | https://en.wikipedia.org/wiki/Game_Description_Language |
The followingoutlineis provided as an overview of and topical guide to artificial intelligence:
Artificial intelligence (AI)is intelligence exhibited by machines or software. It is also the name of thescientific fieldwhich studies how to create computers and computer software that are capable of intelligent behavior.
... | https://en.wikipedia.org/wiki/Outline_of_artificial_intelligence |
Multiple-criteria decision-making(MCDM) ormultiple-criteria decision analysis(MCDA) is a sub-discipline ofoperations researchthat explicitly evaluates multiple conflictingcriteriaindecision making(both in daily life and in settings such as business, government and medicine). It is also known asmultiple attribute utilit... | https://en.wikipedia.org/wiki/Multiple-criteria_decision_analysis |
Multi-objective optimizationorPareto optimization(also known asmulti-objective programming,vector optimization,multicriteria optimization, ormultiattribute optimization) is an area ofmultiple-criteria decision makingthat is concerned withmathematical optimization problemsinvolving more than oneobjective functionto be o... | https://en.wikipedia.org/wiki/Multi-objective_optimization |
Inmultiple criteria decision aiding(MCDA),multicriteria classification(or sorting) involves problems where a finite set of alternative actions should be assigned into a predefined set of preferentially ordered categories (classes).[1]For example, credit analysts classify loan applications into risk categories (e.g., ac... | https://en.wikipedia.org/wiki/Multicriteria_classification |
Robot learningis a research field at the intersection ofmachine learningandrobotics. It studies techniques allowing a robot to acquire novel skills or adapt to its environment through learning algorithms. The embodiment of the robot, situated in a physical embedding, provides at the same time specific difficulties (e.g... | https://en.wikipedia.org/wiki/Robot_learning |
Ametaphoris afigure of speechthat, forrhetoricaleffect, directly refers to one thing by mentioning another.[1]It may provide, or obscure, clarity or identify hidden similarities between two different ideas. Metaphors are usually meant to create a likeness or ananalogy.[2]
Analysts group metaphors with other types of f... | https://en.wikipedia.org/wiki/Metaphor |
Analogyis a comparison or correspondence between two things (or two groups of things) because of a third element that they are considered to share.[1]
In logic, it is aninferenceor anargumentfrom one particular to another particular, as opposed todeduction,induction, andabduction. It is also used where at least one of... | https://en.wikipedia.org/wiki/Analogy |
Analogyis a comparison or correspondence between two things (or two groups of things) because of a third element that they are considered to share.[1]
In logic, it is aninferenceor anargumentfrom one particular to another particular, as opposed todeduction,induction, andabduction. It is also used where at least one of... | https://en.wikipedia.org/wiki/Analogical_reasoning |
Primingis a concept inpsychologyandpsycholinguisticsto describe how exposure to onestimulusmay influence a response to a subsequent stimulus, without conscious guidance or intention.[1][2][3]Thepriming effectis the positive or negative effect of a rapidly presented stimulus (priming stimulus) on the processing of a sec... | https://en.wikipedia.org/wiki/Priming_(psychology) |
Inpsychology,affordanceis what the environment offers the individual. Indesign, affordance has a narrower meaning; it refers to possible actions that an actor can readily perceive.
American psychologistJames J. Gibsoncoined the term in his 1966 book,The Senses Considered as Perceptual Systems,[1]and it occurs in many ... | https://en.wikipedia.org/wiki/Affordance |
Problem solvingis the process of achieving a goal by overcoming obstacles, a frequent part of most activities. Problems in need of solutions range from simple personal tasks (e.g. how to turn on an appliance) to complex issues in business and technical fields. The former is an example of simple problem solving (SPS) ad... | https://en.wikipedia.org/wiki/Problem_solving |
Classical conditioning(alsorespondent conditioningandPavlovian conditioning) is a behavioral procedure in which a biologically potentstimulus(e.g. food, a puff of air on the eye, a potential rival) is paired with a neutral stimulus (e.g. the sound of amusical triangle). The termclassical conditioningrefers to the proce... | https://en.wikipedia.org/wiki/Classical_conditioning |
Gavriel Salomon(Hebrew:גבריאל סלומון; October 1938 – January 2016) was an Israelieducational psychologistwho conducted research oncognitionand instruction.[1]He was a Professor Emeritus in the department of education at theUniversity of Haifa.
He served as the Editor in Chief of theEducational Psychologist.[2] | https://en.wikipedia.org/wiki/Gavriel_Salomon |
Instructional scaffoldingis the support given to a student by an instructor throughout the learning process. This support is specifically tailored to each student; this instructional approach allows students to experiencestudent-centered learning, which tends to facilitate more efficient learning than teacher-centered ... | https://en.wikipedia.org/wiki/Instructional_scaffolding |
One-shot learningis anobject categorization problem, found mostly incomputer vision. Whereas mostmachine learning-based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples. The termfew-shot learningis also u... | https://en.wikipedia.org/wiki/One-shot_learning_in_computer_vision |
Incognitive psychology,fast mappingis the term used for the hypothesized mental process whereby a new concept is learned (or a new hypothesis formed) based only on minimal exposure to a given unit of information (e.g., one exposure to a word in an informative context where its referent is present). Fast mapping is tho... | https://en.wikipedia.org/wiki/Fast_mapping |
Explanation-based learning(EBL) is a form ofmachine learningthat exploits a very strong, or even perfect,domaintheory (i.e. a formal theory of an application domain akin to adomain modelinontology engineering, not to be confused with Scott'sdomain theory) in order to make generalizations or form concepts from training ... | https://en.wikipedia.org/wiki/Explanation-based_learning |
Construct validityconcerns how well a set ofindicators represent or reflect a concept that is not directly measurable.[1][2][3]Construct validationis the accumulation of evidence to support the interpretation of what a measure reflects.[1][4][5][6]Modern validity theory defines construct validity as the overarching con... | https://en.wikipedia.org/wiki/Construct_validity |
Inpsychometrics,content validity(also known aslogical validity) refers to the extent to which a measure represents all facets of a given construct. For example, adepressionscale may lack content validity if it only assesses theaffectivedimension of depression but fails to take into account thebehavioraldimension. An el... | https://en.wikipedia.org/wiki/Content_validity |
Analog signal processingis a type ofsignal processingconducted oncontinuousanalog signalsby some analog means (as opposed to the discretedigital signal processingwhere thesignal processingis carried out by a digital process). "Analog" indicates something that is mathematically represented as a set of continuous values.... | https://en.wikipedia.org/wiki/Analog_signal_processing |
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