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Non-negative matrix factorization(NMForNNMF), alsonon-negative matrix approximation[1][2]is a group ofalgorithmsinmultivariate analysisandlinear algebrawhere amatrixVisfactorizedinto (usually) two matricesWandH, with the property that all three matrices have no negative elements. This non-negativity makes the resulting... | https://en.wikipedia.org/wiki/Non-negative_matrix_factorization |
Nonlinear dimensionality reduction, also known asmanifold learning, is any of various related techniques that aim to project high-dimensional data, potentially existing across non-linear manifolds which cannot be adequately captured by linear decomposition methods, onto lower-dimensionallatent manifolds, with the goal ... | https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction |
Oja's learning rule, or simplyOja's rule, named after Finnish computer scientistErkki Oja(Finnish pronunciation:[ˈojɑ],AW-yuh), is a model of how neurons in the brain or inartificial neural networkschange connection strength, or learn, over time. It is a modification of the standardHebb's Rulethat, through multiplicati... | https://en.wikipedia.org/wiki/Oja%27s_rule |
Thepoint distribution modelis a model for representing the mean geometry of a shape and some statistical modes of geometric variation inferred from a training set of shapes.
The point distribution model concept has been developed by Cootes,[1]Tayloret al.[2]and became a standard incomputer visionfor thestatistical stu... | https://en.wikipedia.org/wiki/Point_distribution_model |
Instatistics,principal component regression(PCR) is aregression analysistechnique that is based onprincipal component analysis(PCA). PCR is a form ofreduced rank regression.[1]More specifically, PCR is used forestimatingthe unknownregression coefficientsin astandard linear regression model.
In PCR, instead of regressi... | https://en.wikipedia.org/wiki/Principal_component_regression |
Intime series analysis,singular spectrum analysis(SSA) is anonparametricspectral estimationmethod. It combines elements of classicaltime seriesanalysis,multivariate statistics, multivariate geometry,dynamical systemsandsignal processing. Its roots lie in the classical Karhunen (1946)–Loève (1945, 1978)spectral decompos... | https://en.wikipedia.org/wiki/Singular_spectrum_analysis |
Sparse principal component analysis(SPCA or sparse PCA) is a technique used in statistical analysis and, in particular, in the analysis ofmultivariatedata sets. It extends the classic method ofprincipal component analysis(PCA) for the reduction of dimensionality of data by introducing sparsity structures to the input v... | https://en.wikipedia.org/wiki/Sparse_PCA |
Transform codingis a type ofdata compressionfor "natural" data likeaudiosignalsor photographicimages. The transformation is typically lossless (perfectly reversible) on its own but is used to enable better (more targeted)quantization, which then results in a lower quality copy of the original input (lossy compression).... | https://en.wikipedia.org/wiki/Transform_coding |
Weighted least squares(WLS), also known asweighted linear regression,[1][2]is a generalization ofordinary least squaresandlinear regressionin which knowledge of the unequalvarianceof observations (heteroscedasticity) is incorporated into the regression.
WLS is also a specialization ofgeneralized least squares, when all... | https://en.wikipedia.org/wiki/Weighted_least_squares |
Asigmoid functionis anymathematical functionwhosegraphhas a characteristic S-shaped orsigmoid curve.
A common example of a sigmoid function is thelogistic function, which is defined by the formula[1]
Other sigmoid functions are given in theExamples section. In some fields, most notably in the context ofartificial neu... | https://en.wikipedia.org/wiki/Sigmoid_function |
In statistics, atobit modelis any of a class ofregression modelsin which the observed range of thedependent variableiscensoredin some way.[1]The term was coined byArthur Goldbergerin reference toJames Tobin,[2][a]who developed the model in 1958 to mitigate the problem ofzero-inflateddata for observations of household e... | https://en.wikipedia.org/wiki/Tobit_model |
Alayerin a deep learning model is a structure ornetwork topologyin the model's architecture, which takes information from the previous layers and then passes it to the next layer.
The first type of layer is theDense layer, also called thefully-connected layer,[1][2][3]and is used for abstract representations of input ... | https://en.wikipedia.org/wiki/Layer_(deep_learning) |
Alogistic functionorlogistic curveis a common S-shaped curve (sigmoid curve) with the equation
f(x)=L1+e−k(x−x0){\displaystyle f(x)={\frac {L}{1+e^{-k(x-x_{0})}}}}
where
The logistic function has domain thereal numbers, the limit asx→−∞{\displaystyle x\to -\infty }is 0, and the limit asx→+∞{\displaystyle x\to +\inft... | https://en.wikipedia.org/wiki/Logistic_function |
Stability, also known asalgorithmic stability, is a notion incomputational learning theoryof how amachine learning algorithmoutput is changed with small perturbations to its inputs. A stable learning algorithm is one for which the prediction does not change much when the training data is modified slightly. For instance... | https://en.wikipedia.org/wiki/Stability_(learning_theory) |
Least absolute deviations(LAD), also known asleast absolute errors(LAE),least absolute residuals(LAR), orleast absolute values(LAV), is a statisticaloptimality criterionand a statisticaloptimizationtechnique based onminimizingthesum of absolute deviations(alsosum of absolute residualsorsum of absolute errors) or theL1n... | https://en.wikipedia.org/wiki/Least_absolute_deviations |
Taxicab geometryorManhattan geometryisgeometrywhere the familiarEuclidean distanceis ignored, and thedistancebetween twopointsis instead defined to be the sum of theabsolute differencesof their respectiveCartesian coordinates, a distance function (ormetric) called thetaxicab distance,Manhattan distance, orcity block di... | https://en.wikipedia.org/wiki/Taxicab_geometry |
Themean absolute percentage error(MAPE), also known asmean absolute percentage deviation(MAPD), is a measure of prediction accuracy of a forecasting method instatistics. It usually expresses the accuracy as a ratio defined by the formula:
whereAtis the actual value andFtis the forecast value. Their difference is divid... | https://en.wikipedia.org/wiki/Mean_absolute_percentage_error |
Thesymmetric mean absolute percentage error(SMAPEorsMAPE) is an accuracy measure based on percentage (or relative) errors. It is usually defined[citation needed]as follows:
whereAtis the actual value andFtis the forecast value.
Theabsolute differencebetweenAtandFtis divided by half the sum of absolute values of the a... | https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error |
Data dredging(also known asdata snoopingorp-hacking)[1][a]is the misuse ofdata analysisto find patterns in data that can be presented asstatistically significant, thus dramatically increasing and understating the risk offalse positives. This is done by performing manystatistical testson the data and only reporting thos... | https://en.wikipedia.org/wiki/Data_dredging |
In machine learning,feature selectionis the process of selecting a subset of relevantfeatures(variables, predictors) for use in model construction. Feature selection techniques are used for several reasons:
The central premise when using feature selection is that data sometimes contains features that areredundantorirr... | https://en.wikipedia.org/wiki/Feature_selection |
Instatistical analysis,Freedman's paradox,[1][2]named afterDavid Freedman, is a problem inmodel selectionwherebypredictor variableswith no relationship to the dependent variable can pass tests of significance – both individually via a t-test, and jointly via an F-test for the significance of the regression. Freedman de... | https://en.wikipedia.org/wiki/Freedman%27s_paradox |
Thegoodness of fitof astatistical modeldescribes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used instatistical hypothesis testing, e.g. totest for normalityofres... | https://en.wikipedia.org/wiki/Goodness_of_fit |
Inprobability theoryand related fields, thelife-time of correlationmeasures the timespan over which there is appreciableautocorrelationorcross-correlationinstochastic processes.
Thecorrelation coefficientρ, expressed as anautocorrelation functionorcross-correlation function, depends on the lag-time between the times b... | https://en.wikipedia.org/wiki/Life-time_of_correlation |
Researcher degrees of freedomis a concept referring to the inherent flexibility involved in the process of designing and conducting ascientific experiment, and in analyzing its results. The term reflects the fact that researchers can choose between multiple ways of collecting and analyzing data, and these decisions can... | https://en.wikipedia.org/wiki/Researcher_degrees_of_freedom |
Inphilosophy,Occam's razor(also spelledOckham's razororOcham's razor;Latin:novacula Occami) is theproblem-solvingprinciple that recommends searching for explanations constructed with the smallest possible set of elements. It is also known as theprinciple of parsimonyor thelaw of parsimony(Latin:lex parsimoniae). Attrib... | https://en.wikipedia.org/wiki/Occam%27s_razor |
Helmut Norpoth(born 1943) is an American political scientist and professor ofpolitical scienceatStony Brook University. Norpoth is best known for developing the Primary Model to predictUnited States presidential elections. Norpoth's model has successfully matched the results of 25 out of 29 United States presidential e... | https://en.wikipedia.org/wiki/Helmut_Norpoth#"Primary_Model"_for_US_presidential_elections |
InVapnik–Chervonenkis theory, theVapnik–Chervonenkis (VC) dimensionis a measure of the size (capacity, complexity, expressive power, richness, or flexibility) of a class of sets. The notion can be extended to classes of binary functions. It is defined as thecardinalityof the largest set of points that the algorithm can... | https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_dimension |
Inmachine learning, aneural scaling lawis an empiricalscaling lawthat describes howneural networkperformance changes as key factors are scaled up or down. These factors typically include the number of parameters,training datasetsize,[1][2]and training cost.
In general, adeep learningmodel can be characterized by four ... | https://en.wikipedia.org/wiki/Broken_Neural_Scaling_Law |
Coordinate descentis anoptimization algorithmthat successively minimizes along coordinate directions to find the minimum of afunction. At each iteration, the algorithm determines acoordinateor coordinate block via a coordinate selection rule, then exactly or inexactly minimizes over the corresponding coordinate hyperpl... | https://en.wikipedia.org/wiki/Coordinate_descent |
Incomputer science,online machine learningis a method ofmachine learningin which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entiretraining data setat once... | https://en.wikipedia.org/wiki/Online_machine_learning |
Stochastic hill climbingis a variant of the basichill climbingmethod. While basic hill climbing always chooses the steepest uphill move, "stochastic hill climbing chooses atrandomfrom among the uphill moves; the probability of selection can vary with thesteepnessof the uphill move."[1]
Thisartificial intelligence-rela... | https://en.wikipedia.org/wiki/Stochastic_hill_climbing |
(Stochastic) variance reductionis an algorithmic approach to minimizing functions that can be decomposed into finite sums. By exploiting the finite sum structure, variance reduction techniques are able to achieve convergence rates that are impossible to achieve with methods that treat the objective as an infinite sum, ... | https://en.wikipedia.org/wiki/Stochastic_variance_reduction |
Collective intelligenceCollective actionSelf-organized criticalityHerd mentalityPhase transitionAgent-based modellingSynchronizationAnt colony optimizationParticle swarm optimizationSwarm behaviour
Social network analysisSmall-world networksCentralityMotifsGraph theoryScalingRobustnessSystems biologyDynamic networks
... | https://en.wikipedia.org/wiki/Emergence |
Biocyberneticsis the application ofcyberneticsto biological science disciplines such asneurologyand multicellular systems. Biocybernetics plays a major role insystems biology, seeking to integrate different levels of information to understand how biological systems function. The field ofcyberneticsitself has origins in... | https://en.wikipedia.org/wiki/Biological_cybernetics |
Bio-inspired computing, short forbiologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates toconnectionism,social behavior, andemergence. Withincomputer science, bio-inspired computing relates to artificial intelligence and machine learning. ... | https://en.wikipedia.org/wiki/Biologically-inspired_computing |
Incoding theory, aconstant-weight code, also called anm-of-ncodeorm-out-of-ncode, is anerror detection and correctioncode where all codewords share the sameHamming weight.
Theone-hotcode and thebalanced codeare two widely used kinds of constant-weight code.
The theory is closely connected to that ofdesigns(such ast-de... | https://en.wikipedia.org/wiki/Constant-weight_code |
Atwo-out-of-five codeis aconstant-weight codethat provides exactly ten possible combinations of two bits, and is thus used for representing thedecimal digitsusing fivebits.[1]Each bit is assigned a weight, such that the set bits sum to the desired value, with an exception for zero.
According toFederal Standard 1037C:
... | https://en.wikipedia.org/wiki/Two-out-of-five_code |
Bi-quinary coded decimalis anumeral encoding schemeused in manyabacusesand in someearly computers, notably theColossus.[2]The termbi-quinaryindicates that the code comprises both a two-state (bi) and a five-state (quinary) component. The encoding resembles that used by many abacuses, with four beads indicating the five... | https://en.wikipedia.org/wiki/Bi-quinary_coded_decimal |
Thereflected binary code(RBC), also known asreflected binary(RB) orGray codeafterFrank Gray, is an ordering of thebinary numeral systemsuch that two successive values differ in only onebit(binary digit).
For example, the representation of the decimal value "1" in binary would normally be "001", and "2" would be "010".... | https://en.wikipedia.org/wiki/Gray_code |
Inmathematics, theKronecker delta(named afterLeopold Kronecker) is afunctionof twovariables, usually just non-negativeintegers. The function is 1 if the variables are equal, and 0 otherwise:δij={0ifi≠j,1ifi=j.{\displaystyle \delta _{ij}={\begin{cases}0&{\text{if }}i\neq j,\\1&{\text{if }}i=j.\end{cases}}}or with use of... | https://en.wikipedia.org/wiki/Kronecker_delta |
In mathematics, theindicator vector,characteristic vector, orincidence vectorof asubsetTof asetSis the vectorxT:=(xs)s∈S{\displaystyle x_{T}:=(x_{s})_{s\in S}}such thatxs=1{\displaystyle x_{s}=1}ifs∈T{\displaystyle s\in T}andxs=0{\displaystyle x_{s}=0}ifs∉T.{\displaystyle s\notin T.}
IfSiscountableand its elements are... | https://en.wikipedia.org/wiki/Indicator_vector |
Inlinear algebra, amatrix unitis amatrixwith only one nonzero entry with value 1.[1][2]The matrix unit with a 1 in theith row andjth column is denoted asEij{\displaystyle E_{ij}}. For example, the 3 by 3 matrix unit withi= 1 andj= 2 isE12=[010000000]{\displaystyle E_{12}={\begin{bmatrix}0&1&0\\0&0&0\\0&0&0\end{bmatrix}... | https://en.wikipedia.org/wiki/Single-entry_vector |
Theunary numeral systemis the simplest numeral system to representnatural numbers:[1]to represent a numberN, a symbol representing 1 is repeatedNtimes.[2]
In the unary system, the number0(zero) is represented by theempty string, that is, the absence of a symbol. Numbers 1, 2, 3, 4, 5, 6, ... are represented in unary a... | https://en.wikipedia.org/wiki/Unary_numeral_system |
Nonparametric regressionis a form ofregression analysiswhere the predictor does not take a predetermined form but is completely constructed using information derived from the data. That is, noparametric equationis assumed for the relationship betweenpredictorsand dependent variable. A largersamplesize is needed to buil... | https://en.wikipedia.org/wiki/Nonparametric_regression |
Ridge regression(also known asTikhonov regularization, named forAndrey Tikhonov) is a method of estimating thecoefficientsof multiple-regression modelsin scenarios where the independent variables are highly correlated.[1]It has been used in many fields including econometrics, chemistry, and engineering.[2]It is a metho... | https://en.wikipedia.org/wiki/Tikhonov_regularization |
Inmachine learning(ML),feature learningorrepresentation learning[2]is a set of techniques that allow a system to automatically discover the representations needed forfeaturedetection or classification from raw data. This replaces manualfeature engineeringand allows a machine to both learn the features and use them to p... | https://en.wikipedia.org/wiki/Representation_learning |
Sparse dictionary learning(also known assparse codingorSDL) is arepresentation learningmethod which aims to find asparserepresentation of the input data in the form of alinear combinationof basic elements as well as those basic elements themselves. These elements are calledatoms, and they compose adictionary. Atoms in ... | https://en.wikipedia.org/wiki/Sparse_dictionary_learning |
Inmathematicsandmachine learning, thesoftplusfunction is
It is a smooth approximation (in fact, ananalytic function) to theramp function, which is known as therectifierorReLU (rectified linear unit)in machine learning. For large negativex{\displaystyle x}it islog(1+ex)=log(1+ϵ)⪆log1=0{\displaystyle \log(1+e^{x})=\l... | https://en.wikipedia.org/wiki/Softplus |
Instatistics,multinomial logistic regressionis aclassificationmethod that generalizeslogistic regressiontomulticlass problems, i.e. with more than two possible discrete outcomes.[1]That is, it is a model that is used to predict the probabilities of the different possible outcomes of acategorically distributeddependent ... | https://en.wikipedia.org/wiki/Multinomial_logistic_regression |
Inprobabilityandstatistics, theDirichlet distribution(afterPeter Gustav Lejeune Dirichlet), often denotedDir(α){\displaystyle \operatorname {Dir} ({\boldsymbol {\alpha }})}, is a family ofcontinuousmultivariateprobability distributionsparameterized by a vectorαof positivereals. It is a multivariate generalization of t... | https://en.wikipedia.org/wiki/Dirichlet_distribution |
Inphysics, apartition functiondescribes thestatisticalproperties of a system inthermodynamic equilibrium.[citation needed]Partition functions arefunctionsof the thermodynamicstate variables, such as thetemperatureandvolume. Most of the aggregatethermodynamicvariables of the system, such as thetotal energy,free energy,... | https://en.wikipedia.org/wiki/Partition_function_(statistical_mechanics) |
Exponential Tilting(ET),Exponential Twisting, orExponential Change of Measure(ECM) is a distribution shifting technique used in many parts of mathematics.
The different exponential tiltings of a random variableX{\displaystyle X}is known as thenatural exponential familyofX{\displaystyle X}.
Exponential Tilting is used ... | https://en.wikipedia.org/wiki/Exponential_tilting |
ADALINE(Adaptive Linear Neuronor laterAdaptive Linear Element) is an early single-layerartificial neural networkand the name of the physical device that implemented it.[2][3][1][4][5]It was developed by professorBernard Widrowand his doctoral studentMarcian HoffatStanford Universityin 1960. It is based on theperceptron... | https://en.wikipedia.org/wiki/ADALINE |
Bio-inspired computing, short forbiologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates toconnectionism,social behavior, andemergence. Withincomputer science, bio-inspired computing relates to artificial intelligence and machine learning. ... | https://en.wikipedia.org/wiki/Bio-inspired_computing |
TheBlue Brain Projectwas a Swiss brain research initiative that aimed to create adigital reconstructionof the mouse brain. The project was founded in May 2005 by the Brain Mind Institute ofÉcole Polytechnique Fédérale de Lausanne(EPFL) in Switzerland. The project ended in December 2024. Its mission was to use biologica... | https://en.wikipedia.org/wiki/Blue_Brain_Project |
Catastrophic interference, also known ascatastrophic forgetting, is the tendency of anartificial neural networkto abruptly and drastically forget previously learned information upon learning new information.[1][2]
Neural networks are an important part of theconnectionistapproach tocognitive science. The issue of catas... | https://en.wikipedia.org/wiki/Catastrophic_interference |
Acognitive architectureis both a theory about the structure of thehuman mindand to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) andcomputational cognitive science.[1]These formalizedmodelscan be used to further refine comprehensive theories ofcognitionand serve as th... | https://en.wikipedia.org/wiki/Cognitive_architecture |
Connectionist expert systemsareartificial neural network(ANN) basedexpert systemswhere the ANN generates inferencing rules e.g., fuzzy-multi layerperceptronwhere linguistic and natural form of inputs are used. Apart from that, roughset theorymay be used for encoding knowledge in the weights better and alsogenetic algor... | https://en.wikipedia.org/wiki/Connectionist_expert_system |
Connectomicsis the production and study ofconnectomes, which are comprehensive maps ofconnectionswithin anorganism'snervous system. Study of neuronal wiring diagrams looks at how they contribute to the health and behavior of an organism.
There are two very different types of connectomes; microscale and macroscale. Mi... | https://en.wikipedia.org/wiki/Connectomics |
Deep image prioris a type ofconvolutional neural networkused to enhance a given image with no prior training data other than the image itself.
A neural network is randomly initialized and used as prior to solveinverse problemssuch asnoise reduction,super-resolution, andinpainting. Image statistics are captured by the s... | https://en.wikipedia.org/wiki/Deep_image_prior |
Digital morphogenesisis a type of generative art in which complex shape development, ormorphogenesis, is enabled by computation. This concept is applicable in many areas of design, art, architecture, and modeling. The concept was originally developed in the field ofbiology, later ingeology,geomorphology, andarchitectu... | https://en.wikipedia.org/wiki/Digital_morphogenesis |
In computer strategy games, for example inshogiandchess, anefficiently updatable neural network(NNUE, a Japanese wordplay onNue, sometimes stylised asƎUИИ) is aneural network-basedevaluation functionwhose inputs arepiece-square tables, or variants thereof like the king-piece-square table.[1]NNUE is used primarily for t... | https://en.wikipedia.org/wiki/Efficiently_updatable_neural_network |
Evolutionary algorithms(EA) reproduce essential elements of thebiological evolutionin acomputer algorithmin order to solve “difficult” problems, at leastapproximately, for which no exact or satisfactory solution methods are known. They belong to the class ofmetaheuristicsand are asubsetofpopulation based bio-inspired a... | https://en.wikipedia.org/wiki/Evolutionary_algorithm |
Ingeometry, afamily of curvesis asetofcurves, each of which is given by afunctionorparametrizationin which one or more of theparametersis variable. In general, the parameter(s) influence the shape of the curve in a way that is more complicated than a simplelinear transformation. Sets of curves given by animplicit relat... | https://en.wikipedia.org/wiki/Family_of_curves |
Incomputer scienceandoperations research, agenetic algorithm(GA) is ametaheuristicinspired by the process ofnatural selectionthat belongs to the larger class ofevolutionary algorithms(EA).[1]Genetic algorithms are commonly used to generate high-quality solutions tooptimizationandsearch problemsvia biologically inspired... | https://en.wikipedia.org/wiki/Genetic_algorithm |
Hyperdimensional computing(HDC) is an approach to computation, particularlyArtificial General Intelligence. HDC is motivated by the observation that thecerebellum cortexoperates on high-dimensional data representations.[1]In HDC, information is thereby represented as a hyperdimensional (long)vectorcalled a hypervector.... | https://en.wikipedia.org/wiki/Hyperdimensional_computing |
Artificial neural networksare a class of models used inmachine learning, and inspired bybiological neural networks. They are the core component of moderndeep learningalgorithms. Computation in artificial neural networks is usually organized into sequential layers ofartificial neurons. The number of neurons in a layer i... | https://en.wikipedia.org/wiki/Large_width_limits_of_neural_networks |
The followingoutlineis provided as an overview of, and topical guide to, machine learning:
Machine learning(ML) is a subfield ofartificial intelligencewithincomputer sciencethat evolved from the study ofpattern recognitionandcomputational learning theory.[1]In 1959,Arthur Samueldefined machine learning as a "field of ... | https://en.wikipedia.org/wiki/List_of_machine_learning_concepts |
Amemristor(/ˈmɛmrɪstər/; aportmanteauofmemory resistor) is a non-lineartwo-terminalelectrical componentrelatingelectric chargeand magneticflux linkage. It was described and named in 1971 byLeon Chua, completing a theoretical quartet of fundamental electrical components which also comprises theresistor,capacitorandinduc... | https://en.wikipedia.org/wiki/Memristor |
Neural gasis anartificial neural network, inspired by theself-organizing mapand introduced in 1991 byThomas MartinetzandKlaus Schulten.[1]The neural gas is a simple algorithm for finding optimal data representations based onfeature vectors. The algorithm was coined "neural gas" because of the dynamics of the feature ve... | https://en.wikipedia.org/wiki/Neural_gas |
Neural network softwareis used tosimulate,research,develop, and applyartificial neural networks, software concepts adapted frombiological neural networks, and in some cases, a wider array ofadaptive systemssuch asartificial intelligenceandmachine learning.
Neural network simulators are software applications that are u... | https://en.wikipedia.org/wiki/Neural_network_software |
Anoptical neural networkis a physical implementation of anartificial neural networkwithoptical components. Early optical neural networks used a photorefractiveVolume hologramto interconnect arrays of input neurons to arrays of output with synaptic weights in proportion to the multiplexed hologram's strength.[2]Volume ... | https://en.wikipedia.org/wiki/Optical_neural_network |
Connectionismis an approach to the study of human mental processes and cognition that utilizes mathematical models known as connectionist networks or artificial neural networks.[1]
Connectionism has had many "waves" since its beginnings. The first wave appeared 1943 withWarren Sturgis McCullochandWalter Pittsboth focu... | https://en.wikipedia.org/wiki/Parallel_distributed_processing |
Thephilosophy of artificial intelligenceis a branch of thephilosophy of mindand thephilosophy of computer science[1]that exploresartificial intelligenceand its implications for knowledge and understanding ofintelligence,ethics,consciousness,epistemology,[2]andfree will.[3][4]Furthermore, the technology is concerned wit... | https://en.wikipedia.org/wiki/Philosophy_of_artificial_intelligence |
Quantum neural networksarecomputational neural networkmodels which are based on the principles ofquantum mechanics. The first ideas on quantum neural computation were published independently in 1995 bySubhash Kakand Ron Chrisley,[1][2]engaging with the theory ofquantum mind, which posits that quantum effects play a rol... | https://en.wikipedia.org/wiki/Quantum_neural_network |
Spiking neural networks(SNNs) areartificial neural networks(ANN) that mimic natural neural networks.[1]These models leverage timing of discrete spikes as the main information carrier.[2]
In addition toneuronalandsynapticstate, SNNs incorporate the concept of time into their operating model. The idea is thatneuronsin t... | https://en.wikipedia.org/wiki/Spiking_neural_network |
Atensor product network, inartificial neural networks, is a network that exploits the properties oftensorsto modelassociativeconcepts such asvariableassignment.Orthonormal vectorsare chosen to model the ideas (such as variable names and target assignments), and the tensor product of thesevectorsconstruct a network whos... | https://en.wikipedia.org/wiki/Tensor_product_network |
AlexNetis aconvolutional neural networkarchitecture developed for image classification tasks, notably achieving prominence through its performance in theImageNetLarge Scale Visual Recognition Challenge (ILSVRC). It classifies images into 1,000 distinct object categories and is regarded as the first widely recognized ap... | https://en.wikipedia.org/wiki/AlexNet |
Aconvolutional neural network(CNN) is a type offeedforward neural networkthat learnsfeaturesviafilter(or kernel) optimization. This type ofdeep learningnetwork has been applied to process and makepredictionsfrom many different types of data including text, images and audio.[1]Convolution-based networks are the de-facto... | https://en.wikipedia.org/wiki/Convolutional_neural_network#Dropout |
Hierarchicalclassificationis a system of grouping things according to a hierarchy.[1]
In the field ofmachine learning, hierarchical classification is sometimes referred to asinstance space decomposition,[2]which splits a completemulti-classproblem into a set of smaller classification problems.
Thisartificial intellig... | https://en.wikipedia.org/wiki/Hierarchical_classification |
TheLeiden algorithmis a community detection algorithm developed by Traaget al[1]atLeiden University. It was developed as a modification of theLouvain method. Like the Louvain method, the Leiden algorithm attempts to optimizemodularityin extracting communities from networks; however, it addresses key issues present in t... | https://en.wikipedia.org/wiki/Leiden_algorithm |
Collective intelligenceCollective actionSelf-organized criticalityHerd mentalityPhase transitionAgent-based modellingSynchronizationAnt colony optimizationParticle swarm optimizationSwarm behaviour
Social network analysisSmall-world networksCentralityMotifsGraph theoryScalingRobustnessSystems biologyDynamic networks
... | https://en.wikipedia.org/wiki/Network_science |
Inartificial intelligenceresearch,commonsense knowledgeconsists of facts about the everyday world, such as "Lemons are sour", or "Cows say moo", that all humans are expected to know. It is currently an unsolved problem inartificial general intelligence. The first AI program to address common sense knowledge wasAdvice ... | https://en.wikipedia.org/wiki/Commonsense_knowledge_bases |
Aconcept maporconceptual diagramis adiagramthat depicts suggested relationships betweenconcepts.[1]Concept maps may be used byinstructional designers,engineers,technical writers, and others to organize and structureknowledge.
A concept map typically represents ideas and information as boxes or circles, which it connec... | https://en.wikipedia.org/wiki/Concept_map |
Ininformation scienceandontology, aclassification schemeis an arrangement of classes or groups of classes. The activity of developing the schemes bears similarity totaxonomy, but with perhaps a more theoretical bent, as a single classification scheme can be applied over a widesemantic spectrumwhile taxonomies tend to b... | https://en.wikipedia.org/wiki/Classification_scheme_(information_science) |
Folksonomyis aclassification systemin whichend usersapply publictagsto online items, typically to make those items easier for themselves or others to find later. Over time, this can give rise to a classification system based on those tags and how often they are applied or searched for, in contrast to ataxonomicclassifi... | https://en.wikipedia.org/wiki/Folksonomy |
Ininformation science,formal concept analysis(FCA) is aprincipled wayof deriving aconcept hierarchyor formalontologyfrom a collection ofobjectsand theirproperties. Each concept in the hierarchy represents the objects sharing some set of properties; and each sub-concept in the hierarchy represents asubsetof the objects ... | https://en.wikipedia.org/wiki/Formal_concept_analysis |
Inphilosophy, the termformal ontologyis used to refer to anontologydefined byaxiomsin aformal languagewith the goal to provide anunbiased(domain- and application-independent) view onreality, which can help the modeler ofdomain- or application-specificontologiesto avoid possibly erroneous ontological assumptions encount... | https://en.wikipedia.org/wiki/Formal_ontology |
TheGeneral Concept Lattice(GCL) proposes a novel general construction of concept hierarchy from formal context, where the conventional Formal Concept Lattice based onFormal Concept Analysis(FCA) only serves as a substructure.[1][2][3]
The formal context is a data table ofheterogeneous relationsillustrating how object... | https://en.wikipedia.org/wiki/General_Concept_Lattice |
Inknowledge representation and reasoning, aknowledge graphis aknowledge basethat uses agraph-structureddata modelortopologyto represent and operate ondata. Knowledge graphs are often used to store interlinked descriptions ofentities– objects, events, situations or abstract concepts – while also encoding the free-form... | https://en.wikipedia.org/wiki/Knowledge_graph |
All definitions tacitly require thehomogeneous relationR{\displaystyle R}betransitive: for alla,b,c,{\displaystyle a,b,c,}ifaRb{\displaystyle aRb}andbRc{\displaystyle bRc}thenaRc.{\displaystyle aRc.}A term's definition may require additional properties that are not listed in this table.
Alatticeis an abstract structur... | https://en.wikipedia.org/wiki/Lattice_(order) |
Ontologyis the philosophical study ofbeing. It is traditionally understood as the subdiscipline ofmetaphysicsfocused on the most general features ofreality. As one of the most fundamental concepts, being encompasses all of reality and everyentitywithin it. To articulate the basic structure of being, ontology examines t... | https://en.wikipedia.org/wiki/Ontology |
Ontology alignment, orontology matching, is the process of determining correspondences betweenconceptsinontologies. A set of correspondences is also called an alignment. The phrase takes on a slightly different meaning, incomputer science,cognitive scienceorphilosophy.
Forcomputer scientists, concepts are expressed as... | https://en.wikipedia.org/wiki/Ontology_alignment |
Anontology chartis a type ofchartused insemioticsandsoftware engineeringto illustrate anontology.
The nodes of an ontology chart represent universalaffordancesand rarely representparticulars. The exception is the root which is a particular agent often labelled ‘society’ and located on the extreme left of an ontology c... | https://en.wikipedia.org/wiki/Ontology_chart |
TheOpen Semantic Framework(OSF) is an integratedsoftware stackusingsemantic technologiesforknowledge management.[1]It has a layered architecture that combines existingopen sourcesoftwarewith additional open source components developed specifically to provide a completeWeb application framework. OSF is made available un... | https://en.wikipedia.org/wiki/Open_Semantic_Framework |
The term "soft ontology", coined byEli Hirschin 1993, refers to the embracing or reconciling of apparentontologicaldifferences, by means of relevant distinctions and contextual analyses.
Hirsch used the term to broaden and expand on whatWilliam Jamesdiscussed in his landmark 1907 work inepistemology,Pragmatism. James... | https://en.wikipedia.org/wiki/Soft_ontology |
Terminology extraction(also known astermextraction,glossaryextraction, termrecognition, or terminologymining) is a subtask ofinformation extraction. The goal of terminology extraction is to automatically extract relevant terms from a givencorpus.[1]
In thesemantic webera, a growing number of communities and networked ... | https://en.wikipedia.org/wiki/Terminology_extraction |
Incomputer science, aweak ontologyis anontologythat is not sufficiently rigorous to allow software to infer new facts without intervention by humans (the end users of the software system). In other words, it does not contain sufficient literal information.[1]
By this standard – which evolved asartificial intelligencem... | https://en.wikipedia.org/wiki/Weak_ontology |
TheWeb Ontology Language(OWL) is a family ofknowledge representationlanguages for authoringontologies. Ontologies are a formal way to describetaxonomiesand classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing r... | https://en.wikipedia.org/wiki/Web_Ontology_Language |
Variational Bayesian methodsare a family of techniques for approximating intractableintegralsarising inBayesian inferenceandmachine learning. They are typically used in complexstatistical modelsconsisting of observed variables (usually termed "data") as well as unknownparametersandlatent variables, with various sorts ... | https://en.wikipedia.org/wiki/Variational_Bayesian_methods |
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