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Inmachine learning, thehinge lossis aloss functionused for trainingclassifiers. The hinge loss is used for "maximum-margin" classification, most notably forsupport vector machines(SVMs).[1]
For an intended outputt= ±1and a classifier scorey, the hinge loss of the predictionyis defined as
Note thaty{\displaystyle y}sh... | https://en.wikipedia.org/wiki/Hinge_loss |
NumPy(pronounced/ˈnʌmpaɪ/NUM-py) is alibraryfor thePython programming language, adding support for large, multi-dimensionalarraysandmatrices, along with a large collection ofhigh-levelmathematicalfunctionsto operate on these arrays.[3]The predecessor of NumPy, Numeric, was originally created byJim Huguninwith contribut... | https://en.wikipedia.org/wiki/Numpy |
Instatistical modeling,regression analysisis a set of statistical processes forestimatingthe relationships between adependent variable(often called theoutcomeorresponsevariable, or alabelin machine learning parlance) and one or more error-freeindependent variables(often calledregressors,predictors,covariates,explanator... | https://en.wikipedia.org/wiki/Regression_analysis |
Principal component analysis(PCA) is alineardimensionality reductiontechnique with applications inexploratory data analysis, visualization anddata preprocessing.
The data islinearly transformedonto a newcoordinate systemsuch that the directions (principal components) capturing the largest variation in the data can be ... | https://en.wikipedia.org/wiki/Principal_component_analysis |
In the context ofartificial neural networks, therectifierorReLU (rectified linear unit) activation function[1][2]is anactivation functiondefined as the non-negative part of its argument, i.e., theramp function:
wherex{\displaystyle x}is the input to aneuron. This is analogous tohalf-wave rectificationinelectrical engi... | https://en.wikipedia.org/wiki/Rectifier_(neural_networks) |
Theactivation functionof a node in anartificial neural networkis a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function isnonlinear.[1]
Modern activation functions include the logistic (sigm... | https://en.wikipedia.org/wiki/Activation_function |
Instatistics,mean absolute error(MAE) is a measure oferrorsbetween paired observations expressing the same phenomenon. Examples ofYversusXinclude comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement. MAE is calcula... | https://en.wikipedia.org/wiki/Mean_absolute_error |
Inmathematical modeling,overfittingis "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably".[1]Anoverfitted modelis amathematical modelthat contains moreparametersthan can be justified... | https://en.wikipedia.org/wiki/Overfitting |
Stochastic gradient descent(often abbreviatedSGD) is aniterativemethod for optimizing anobjective functionwith suitablesmoothnessproperties (e.g.differentiableorsubdifferentiable). It can be regarded as astochastic approximationofgradient descentoptimization, since it replaces the actual gradient (calculated from the e... | https://en.wikipedia.org/wiki/Stochastic_gradient_descent |
Aneural networkis a group of interconnected units calledneuronsthat send signals to one another. Neurons can be eitherbiological cellsormathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks. There are two main types of neural networks.
In the context of ... | https://en.wikipedia.org/wiki/Neural_network#Feedforward_neural_networks |
Indigital circuitsandmachine learning, aone-hotis a group ofbitsamong which the legal combinations of values are only those with a single high (1) bit and all the others low (0).[1]A similar implementation in which all bits are '1' except one '0' is sometimes calledone-cold.[2]Instatistics,dummy variablesrepresent a si... | https://en.wikipedia.org/wiki/One-hot_encoding |
Adecision treeis adecision supportrecursive partitioning structure that uses atree-likemodelof decisions and their possible consequences, includingchanceevent outcomes, resource costs, andutility. It is one way to display analgorithmthat only contains conditional control statements.
Decision trees are commonly used in... | https://en.wikipedia.org/wiki/Decision_tree#Applications |
Adecision treeis adecision supportrecursive partitioning structure that uses atree-likemodelof decisions and their possible consequences, includingchanceevent outcomes, resource costs, andutility. It is one way to display analgorithmthat only contains conditional control statements.
Decision trees are commonly used in... | https://en.wikipedia.org/wiki/Decision_tree#Interpretability |
Instatisticsandmachine learning,lasso(least absolute shrinkage and selection operator; alsoLasso,LASSOorL1 regularization)[1]is aregression analysismethod that performs bothvariable selectionandregularizationin order to enhance the prediction accuracy and interpretability of the resultingstatistical model. The lasso me... | https://en.wikipedia.org/wiki/Lasso_(statistics) |
t-distributed stochastic neighbor embedding(t-SNE) is astatisticalmethod for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map. It is based on Stochastic Neighbor Embedding originally developed byGeoffrey Hintonand Sam Roweis,[1]where Laurens van der Maaten and Hint... | https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding |
Anautoencoderis a type ofartificial neural networkused to learnefficient codingsof unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder lear... | https://en.wikipedia.org/wiki/Autoencoder |
Thesoftmax function,also known assoftargmax[1]: 184ornormalized exponential function,[2]: 198converts a vector ofKreal numbers into aprobability distributionofKpossible outcomes. It is a generalization of thelogistic functionto multiple dimensions, and is used inmultinomial logistic regression. The softmax function is ... | https://en.wikipedia.org/wiki/Softmax_function |
Inmachine learning, aneural network(alsoartificial neural networkorneural net, abbreviatedANNorNN) is a computational model inspired by the structure and functions of biological neural networks.[1][2]
A neural network consists of connected units or nodes calledartificial neurons, which loosely model theneuronsin the b... | https://en.wikipedia.org/wiki/Artificial_neural_network |
PyTorchis amachine learninglibrarybased on theTorchlibrary,[4][5][6]used for applications such ascomputer visionandnatural language processing,[7]originally developed byMeta AIand now part of theLinux Foundationumbrella.[8][9][10][11]It is one of the most populardeep learningframeworks, alongside others such asTensorFl... | https://en.wikipedia.org/wiki/PyTorch |
Batch normalization(also known asbatch norm) is anormalizationtechnique used to make training ofartificial neural networksfaster and more stable by adjusting the inputs to each layer—re-centering them around zero and re-scaling them to a standard size. It was introduced by Sergey Ioffe and Christian Szegedy in 2015.[1]... | https://en.wikipedia.org/wiki/Batch_normalization |
Dropoutanddilution(also calledDropConnect[1]) areregularizationtechniques for reducingoverfittinginartificial neural networksby preventing complex co-adaptations ontraining data. They are an efficient way of performing model averaging with neural networks.[2]Dilutionrefers to randomly decreasing weights towards zero,[3... | https://en.wikipedia.org/wiki/Dropout_%28neural_networks%29 |
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#Stride |
Hyperparametermay refer to: | https://en.wikipedia.org/wiki/Hyperparameter#Validation_set |
Inmachine learning, a common task is the study and construction ofalgorithmsthat can learn from and make predictions ondata.[1]Such algorithms function by making data-driven predictions or decisions,[2]through building amathematical modelfrom input data. These input data used to build the model are usually divided into... | https://en.wikipedia.org/wiki/Test_set |
TheLouvain method for community detectionis agreedy optimizationmethod intended to extract non-overlapping communities from largenetworkscreated byBlondelet al.[1]from theUniversity of Louvain(the source of this method's name).
The inspiration for this method ofcommunity detectionis the optimization ofmodularityas the... | https://en.wikipedia.org/wiki/Louvain_modularity |
Ininformation science, anontologyencompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or alldomains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how ... | https://en.wikipedia.org/wiki/Ontology_(information_science) |
Innatural language processing,latentDirichletallocation(LDA) is aBayesian network(and, therefore, agenerative statistical model) for modeling automatically extracted topics in textual corpora. The LDA is an example of a Bayesiantopic model. In this, observations (e.g., words) are collected into documents, and each word... | https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation |
Latent semantic analysis(LSA) is a technique innatural language processing, in particulardistributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will oc... | https://en.wikipedia.org/wiki/Latent_semantic_analysis |
Information retrieval(IR) incomputingandinformation scienceis the task of identifying and retrievinginformation systemresources that are relevant to aninformation need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based onfull-textor other conte... | https://en.wikipedia.org/wiki/Information_retrieval#Indexing |
MapReduceis aprogramming modeland an associated implementation for processing and generatingbig datasets with aparallelanddistributedalgorithm on acluster.[1][2][3]
A MapReduce program is composed of amapprocedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for ... | https://en.wikipedia.org/wiki/MapReduce |
Crowdsourcinginvolves a large group of dispersed participants contributing or producinggoods or services—including ideas,votes,micro-tasks, and finances—for payment or as volunteers. Contemporary crowdsourcing often involvesdigital platformsto attract and divide work between participants to achieve a cumulative result.... | https://en.wikipedia.org/wiki/Crowdsourcing |
Amajorityis more than half of a total.[1]It is asubsetof asetconsisting of more than half of the set's elements. For example, if a group consists of 31 individuals, a majority would be 16 or more individuals, while having 15 or fewer individuals would not constitute a majority.
A majority is different from, but often ... | https://en.wikipedia.org/wiki/Majority_vote |
Instatistics, anexpectation–maximization(EM)algorithmis aniterative methodto find (local)maximum likelihoodormaximum a posteriori(MAP) estimates ofparametersinstatistical models, where the model depends on unobservedlatent variables.[1]The EM iteration alternates between performing an expectation (E) step, which create... | https://en.wikipedia.org/wiki/Expectation–maximization_algorithm |
PageRank(PR) is analgorithmused byGoogle Searchtorankweb pagesin theirsearch engineresults. It is named after both the term "web page" and co-founderLarry Page. PageRank is a way of measuring the importance of website pages. According to Google:
PageRank works by counting the number and quality of links to a page to d... | https://en.wikipedia.org/wiki/PageRank#Iterative_computation |
Information retrieval(IR) incomputingandinformation scienceis the task of identifying and retrievinginformation systemresources that are relevant to aninformation need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based onfull-textor other conte... | https://en.wikipedia.org/wiki/Information_retrieval#Evaluation_measures |
Apriori[1]is analgorithmfor frequent item set mining andassociation rule learningoverrelational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item se... | https://en.wikipedia.org/wiki/Apriori_algorithm |
Association rule learningis arule-based machine learningmethod for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.[1]In any given transaction with a variety of items, association rules are mea... | https://en.wikipedia.org/wiki/FP-growth |
Instatistics, thePearson correlation coefficient(PCC)[a]is acorrelation coefficientthat measureslinearcorrelation between two sets of data. It is the ratio between thecovarianceof two variables and the product of theirstandard deviations; thus, it is essentially a normalized measurement of the covariance, such that the... | https://en.wikipedia.org/wiki/Pearson_correlation_coefficient |
Ininformation retrieval,tf–idf(alsoTF*IDF,TFIDF,TF–IDF, orTf–idf), short forterm frequency–inverse document frequency, is a measure of importance of a word to adocumentin a collection orcorpus, adjusted for the fact that some words appear more frequently in general.[1]Like the bag-of-words model, it models a document ... | https://en.wikipedia.org/wiki/Tf%E2%80%93idf#Term_frequency |
Cross-validation,[2][3][4]sometimes calledrotation estimation[5][6][7]orout-of-sample testing, is any of various similarmodel validationtechniques for assessing how the results of astatisticalanalysis willgeneralizeto an independent data set.
Cross-validation includesresamplingand sample splitting methods that use diff... | https://en.wikipedia.org/wiki/Cross-validation_(statistics) |
Adata modelis anabstract modelthat organizes elements ofdataandstandardizeshow they relate to one another and to the properties of real-worldentities.[2][3]For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and si... | https://en.wikipedia.org/wiki/Structured_data |
Unstructured data(orunstructured information) is information that either does not have a pre-defineddata modelor is not organized in a pre-defined manner. Unstructured information is typicallytext-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities andambiguitiesthat ma... | https://en.wikipedia.org/wiki/Unstructured_data |
Instatisticalanalysis ofbinary classificationandinformation retrievalsystems, theF-scoreorF-measureis a measure of predictive performance. It is calculated from theprecisionandrecallof the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, inc... | https://en.wikipedia.org/wiki/F-score |
Achi-squared test(alsochi-squareorχ2test) is astatistical hypothesis testused in the analysis ofcontingency tableswhen the sample sizes are large. In simpler terms, this test is primarily used to examine whether two categorical variables (two dimensions of the contingency table) are independent in influencing the test ... | https://en.wikipedia.org/wiki/Chi-squared_test |
Independenceis a fundamental notion inprobability theory, as instatisticsand the theory ofstochastic processes. Twoeventsareindependent,statistically independent, orstochastically independent[1]if, informally speaking, the occurrence of one does not affect the probability of occurrence of the other or, equivalently, do... | https://en.wikipedia.org/wiki/Statistical_independence |
Named-entity recognition(NER) (also known as(named)entity identification,entity chunking, andentity extraction) is a subtask ofinformation extractionthat seeks to locate and classifynamed entitiesmentioned inunstructured textinto pre-defined categories such as person names, organizations, locations,medical codes, time ... | https://en.wikipedia.org/wiki/Named_entity_recognition |
TheResource Description Framework(RDF) is a method to describe and exchangegraphdata. It was originally designed as a data model formetadataby theWorld Wide Web Consortium(W3C). It provides a variety of syntax notations and formats, of which the most widely used is Turtle (Terse RDF Triple Language).
RDF is adirected ... | https://en.wikipedia.org/wiki/Resource_Description_Framework |
Arelational database(RDB[1]) is adatabasebased on therelational modelof data, as proposed byE. F. Coddin 1970.[2]
A RelationalDatabase Management System(RDBMS) is a type of database management system that stores data in a structuredformatusingrowsandcolumns.
Many relational database systems are equipped with the opti... | https://en.wikipedia.org/wiki/Relational_database |
InRDF, ablank node(also calledbnode) is a node in an RDF graph representing a resource for which aURIor literal is not given.[1]The resource represented by a blank node is also called ananonymous resource. According to the RDF standard a blank node can only be used as subject or object of an RDF triple.
Blank nodes ca... | https://en.wikipedia.org/wiki/Blank_node |
Adocument-term matrixis a mathematicalmatrixthat describes the frequency of terms that occur in each document in a collection. In a document-term matrix, rows correspond to documents in the collection and columns correspond to terms. This matrix is a specific instance of adocument-feature matrixwhere "features" may ref... | https://en.wikipedia.org/wiki/Term-document_matrix |
Extensible Markup Language(XML) is amarkup languageandfile formatfor storing, transmitting, and reconstructing data. It defines a set of rules for encodingdocumentsin a format that is bothhuman-readableandmachine-readable. TheWorld Wide Web Consortium's XML 1.0 Specification[2]of 1998[3]and several other related specif... | https://en.wikipedia.org/wiki/XML |
Information retrieval(IR) incomputingandinformation scienceis the task of identifying and retrievinginformation systemresources that are relevant to aninformation need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based onfull-textor other conte... | https://en.wikipedia.org/wiki/Information_retrieval#Relevance |
Hyperlink-Induced Topic Search(HITS; also known ashubs and authorities) is alink analysisalgorithmthat rates Web pages, developed byJon Kleinberg. The idea behind Hubs and Authorities stemmed from a particular insight into the creation of web pages when the Internet was originally forming; that is, certain web pages, k... | https://en.wikipedia.org/wiki/HITS_algorithm |
Incomputer science, aninverted index(also referred to as apostings list,postings file, orinverted file) is adatabase indexstoring a mapping from content, such as words or numbers, to its locations in atable, or in a document or a set of documents (named in contrast to aforward index, which maps from documents to conten... | https://en.wikipedia.org/wiki/Inverted_index |
Collaborative filtering(CF) is, besidescontent-based filtering, one of two major techniques used byrecommender systems.[1]Collaborative filtering has two senses, a narrow one and a more general one.[2]
In the newer, narrower sense, collaborative filtering is a method of making automaticpredictions(filtering) about aus... | https://en.wikipedia.org/wiki/Collaborative_filtering |
Arecommender system (RecSys), or arecommendation system(sometimes replacingsystemwith terms such asplatform,engine, oralgorithm), sometimes only called "the algorithm" or "algorithm"[1]is a subclass ofinformation filtering systemthat provides suggestions for items that are most pertinent to a particular user.[2][3][4]R... | https://en.wikipedia.org/wiki/Content-based_filtering |
TheViterbi algorithmis adynamic programmingalgorithmfor obtaining themaximum a posteriori probability estimateof the mostlikelysequence of hidden states—called theViterbi path—that results in a sequence of observed events. This is done especially in the context ofMarkov information sourcesandhidden Markov models(HMM).
... | https://en.wikipedia.org/wiki/Viterbi_algorithm |
Information retrieval(IR) incomputingandinformation scienceis the task of identifying and retrievinginformation systemresources that are relevant to aninformation need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based onfull-textor other conte... | https://en.wikipedia.org/wiki/Information_retrieval#Inverted_index |
Arecommender system (RecSys), or arecommendation system(sometimes replacingsystemwith terms such asplatform,engine, oralgorithm), sometimes only called "the algorithm" or "algorithm"[1]is a subclass ofinformation filtering systemthat provides suggestions for items that are most pertinent to a particular user.[2][3][4]R... | https://en.wikipedia.org/wiki/Recommender_system |
In themathematicaldiscipline oflinear algebra, amatrix decompositionormatrix factorizationis afactorizationof amatrixinto a product of matrices. There are many different matrix decompositions; each finds use among a particular class of problems.
Innumerical analysis, different decompositions are used to implement effi... | https://en.wikipedia.org/wiki/Matrix_factorization |
RDF Schema(Resource Description Framework Schema, variously abbreviated asRDFS,RDF(S),RDF-S, orRDF/S) is a set of classes with certain properties using theRDFextensibleknowledge representationdata model, providing basic elements for the description ofontologies. It uses various forms of RDF vocabularies, intended to st... | https://en.wikipedia.org/wiki/RDF_Schema#Classes_and_properties |
In computer terminology, ahoneypotis acomputer securitymechanism set to detect, deflect, or, in some manner, counteract attempts at unauthorized use ofinformation systems. Generally, a honeypot consists ofdata(for example, in a network site) that appears to be a legitimate part of the site which contains information or... | https://en.wikipedia.org/wiki/Honeypot_(computing) |
Association rule learningis arule-based machine learningmethod for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.[1]In any given transaction with a variety of items, association rules are mea... | https://en.wikipedia.org/wiki/Association_rule_learning |
Bootstrappingis a procedure for estimating the distribution of an estimator byresampling(oftenwith replacement) one's data or a model estimated from the data.[1]Bootstrapping assigns measures of accuracy (bias, variance,confidence intervals, prediction error, etc.) to sample estimates.[2][3]This technique allows estima... | https://en.wikipedia.org/wiki/Bootstrapping_(statistics) |
Random forestsorrandom decision forestsis anensemble learningmethod forclassification,regressionand other tasks that works by creating a multitude ofdecision treesduring training. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the output is the avera... | https://en.wikipedia.org/wiki/Random_forest |
RDF Schema(Resource Description Framework Schema, variously abbreviated asRDFS,RDF(S),RDF-S, orRDF/S) is a set of classes with certain properties using theRDFextensibleknowledge representationdata model, providing basic elements for the description ofontologies. It uses various forms of RDF vocabularies, intended to st... | https://en.wikipedia.org/wiki/RDF_schema#Range_and_domain |
Adecision treeis adecision supportrecursive partitioning structure that uses atree-likemodelof decisions and their possible consequences, includingchanceevent outcomes, resource costs, andutility. It is one way to display analgorithmthat only contains conditional control statements.
Decision trees are commonly used in... | https://en.wikipedia.org/wiki/Decision_tree#Pruning |
Innatural language processing, aword embeddingis a representation of a word. Theembeddingis used intext analysis. Typically, the representation is areal-valuedvector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning.[1]Word embedd... | https://en.wikipedia.org/wiki/Word_embedding |
RDF Schema(Resource Description Framework Schema, variously abbreviated asRDFS,RDF(S),RDF-S, orRDF/S) is a set of classes with certain properties using theRDFextensibleknowledge representationdata model, providing basic elements for the description ofontologies. It uses various forms of RDF vocabularies, intended to st... | https://en.wikipedia.org/wiki/RDF_Schema |
In computer science, atrie(/ˈtraɪ/,/ˈtriː/ⓘ), also known as adigital treeorprefix tree,[1]is a specializedsearch treedata structure used to store and retrieve strings from a dictionary or set. Unlike abinary search tree, nodes in a trie do not store their associated key. Instead, each node'spositionwithin the trie dete... | https://en.wikipedia.org/wiki/Trie |
Inmathematics, arandom walk, sometimes known as adrunkard's walk, is astochastic processthat describes a path that consists of a succession ofrandomsteps on somemathematical space.
An elementary example of a random walk is the random walk on the integer number lineZ{\displaystyle \mathbb {Z} }which starts at 0, and at... | https://en.wikipedia.org/wiki/Random_walk |
PageRank(PR) is analgorithmused byGoogle Searchtorankweb pagesin theirsearch engineresults. It is named after both the term "web page" and co-founderLarry Page. PageRank is a way of measuring the importance of website pages. According to Google:
PageRank works by counting the number and quality of links to a page to d... | https://en.wikipedia.org/wiki/PageRank |
Collaborative filtering(CF) is, besidescontent-based filtering, one of two major techniques used byrecommender systems.[1]Collaborative filtering has two senses, a narrow one and a more general one.[2]
In the newer, narrower sense, collaborative filtering is a method of making automaticpredictions(filtering) about aus... | https://en.wikipedia.org/wiki/Collaborative_filtering#Matrix_factorization |
Clusteringcan refer to the following:
Incomputing:
Ineconomics:
Ingraph theory: | https://en.wikipedia.org/wiki/Clustering |
Instatisticsandnatural language processing, atopic modelis a type ofstatistical modelfor discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about... | https://en.wikipedia.org/wiki/Topic_modeling |
Information extraction(IE) is the task of automatically extractingstructured informationfromunstructuredand/or semi-structuredmachine-readabledocuments and other electronically represented sources. Typically, this involves processing human language texts by means ofnatural language processing(NLP).[1]Recent activities ... | https://en.wikipedia.org/wiki/Information_extraction |
Bootstrap aggregating, also calledbagging(frombootstrapaggregating) orbootstrapping, is amachine learning(ML)ensemblemeta-algorithmdesigned to improve thestabilityand accuracy of MLclassificationandregressionalgorithms. It also reducesvarianceandoverfitting. Although it is usually applied todecision treemethods, it can... | https://en.wikipedia.org/wiki/Bootstrapping_(machine_learning) |
Inmachine learning,supervised learning(SL) is a paradigm where amodelis trained using input objects (e.g. a vector of predictor variables) and desired output values (also known as asupervisory signal), which are often human-made labels. The training process builds a function that maps new data to expected output values... | https://en.wikipedia.org/wiki/Supervised_learning |
Inmathematical modeling,overfittingis "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably".[1]Anoverfitted modelis amathematical modelthat contains moreparametersthan can be justified... | https://en.wikipedia.org/wiki/Underfitting |
Inlinear algebra, thesingular value decomposition(SVD) is afactorizationof arealorcomplexmatrixinto a rotation, followed by a rescaling followed by another rotation. It generalizes theeigendecompositionof a squarenormal matrixwith an orthonormal eigenbasis to anym×n{\displaystyle m\times n}matrix. It is related to th... | https://en.wikipedia.org/wiki/Singular_value_decomposition |
Schema.orgis a reference website that publishes documentation and guidelines for usingstructured datamark-up on web-pages (in the form ofmicrodata,RDFaorJSON-LD). Its main objective is to standardizeHTMLtags to be used by webmasters for creating rich results (displayed as visual data or infographic tables on search eng... | https://en.wikipedia.org/wiki/Schema.org |
Thetransformeris adeep learningarchitecture that was developed by researchers atGoogleand is based on the multi-headattentionmechanism, which was proposed in the 2017 paper "Attention Is All You Need".[1]Text is converted to numerical representations calledtokens, and each token is converted into a vector via lookup fr... | https://en.wikipedia.org/wiki/Transformer_(machine_learning) |
Attentionis amachine learningmethod that determines the importance of each component in a sequence relative to the other components in that sequence. Innatural language processing, importance is represented by"soft"weights assigned to each word in a sentence. More generally, attention encodes vectors calledtokenembeddi... | https://en.wikipedia.org/wiki/Attention_mechanism |
Incomputer science,merge sort(also commonly spelled asmergesortand asmerge-sort[2]) is an efficient, general-purpose, andcomparison-basedsorting algorithm. Most implementations produce astable sort, which means that the relative order of equal elements is the same in the input and output. Merge sort is adivide-and-conq... | https://en.wikipedia.org/wiki/Merge_sort |
Instatisticsandnatural language processing, atopic modelis a type ofstatistical modelfor discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about... | https://en.wikipedia.org/wiki/Topic_model |
Modularityis a measure of the structure ofnetworksorgraphswhich measures the strength of division of a network into modules (also called groups, clusters or communities). Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules. Modu... | https://en.wikipedia.org/wiki/Modularity_(networks) |
1800s:Martineau·Tocqueville·Marx·Spencer·Le Bon·Ward·Pareto·Tönnies·Veblen·Simmel·Durkheim·Addams·Mead·Weber·Du Bois·Mannheim·Elias
Social network analysis(SNA) is the process of investigating social structures through the use ofnetworksandgraph theory.[1]It characterizes networked structures in terms ofnodes(individ... | https://en.wikipedia.org/wiki/Social_network_analysis |
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_in_information_science |
Decision tree learningis asupervised learningapproach used instatistics,data miningandmachine learning. In this formalism, a classification or regressiondecision treeis used as apredictive modelto draw conclusions about a set of observations.
Tree models where the target variable can take a discrete set of values are ... | https://en.wikipedia.org/wiki/Decision_tree_learning |
Density-based spatial clustering of applications with noise(DBSCAN) is adata clusteringalgorithmproposed byMartin Ester,Hans-Peter Kriegel,Jörg Sander, andXiaowei Xuin 1996.[1]It is adensity-based clusteringnon-parametric algorithm: given a set of points in some space, it groups together points that are closely packed ... | https://en.wikipedia.org/wiki/DBSCAN |
Cluster analysisorclusteringis the data analyzing technique in which task of grouping a set of objects in such a way that objects in the same group (called acluster) are moresimilar(in some specific sense defined by the analyst) to each other than to those in other groups (clusters). It is a main task ofexploratory dat... | https://en.wikipedia.org/wiki/Density-based_clustering |
In the study ofcomplex networks, a network is said to havecommunity structureif the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally. In the particular case ofnon-overlappingcommunity finding, this implies that the networ... | https://en.wikipedia.org/wiki/Community_structure |
ATwitter botor anX botis a type of softwarebotthat controls aTwitter/Xaccount via the TwitterAPI.[1]Thesocial botsoftware may autonomously perform actions such as tweeting, retweeting, liking, following, unfollowing, or direct messaging other accounts.[citation needed]The automation of Twitter accounts is governed by a... | https://en.wikipedia.org/wiki/Twitter_bot |
Ingraph theory, aclique(/ˈkliːk/or/ˈklɪk/) is a subset of vertices of anundirected graphsuch that every two distinct vertices in the clique areadjacent. That is, a clique of a graphG{\displaystyle G}is aninduced subgraphofG{\displaystyle G}that iscomplete. Cliques are one of the basic concepts of graph theory and are u... | https://en.wikipedia.org/wiki/Clique_(graph_theory) |
Latent semantic analysis(LSA) is a technique innatural language processing, in particulardistributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will oc... | https://en.wikipedia.org/wiki/Latent_Semantic_Indexing |
fastTextis a library for learning ofword embeddingsand text classification created byFacebook's AI Research (FAIR) lab.[3][4][5][6]The model allows one to create anunsupervised learningorsupervised learningalgorithm for obtaining vector representations for words. Facebook makes available pretrained models for 294 langu... | https://en.wikipedia.org/wiki/FastText |
Instatistics, anexpectation–maximization(EM)algorithmis aniterative methodto find (local)maximum likelihoodormaximum a posteriori(MAP) estimates ofparametersinstatistical models, where the model depends on unobservedlatent variables.[1]The EM iteration alternates between performing an expectation (E) step, which create... | https://en.wikipedia.org/wiki/Expectation-maximization_algorithm |
Frequent pattern discovery(orFP discovery,FP mining, orFrequent itemset mining) is part ofknowledge discovery in databases,Massive Online Analysis, anddata mining; it describes the task of finding the most frequent and relevantpatternsin large datasets.[1][2]The concept was first introduced for mining transaction datab... | https://en.wikipedia.org/wiki/Frequent_pattern_mining |
TheGirvan–Newman algorithm(named afterMichelle GirvanandMark Newman) is a hierarchical method used to detectcommunitiesincomplex systems.[1]
The Girvan–Newman algorithm detects communities by progressively removing edges from the original network. The connected components of the remaining network are the communities. ... | https://en.wikipedia.org/wiki/Girvan–Newman_algorithm |
Inprobability theory,conditional independencedescribes situations wherein an observation is irrelevant or redundant when evaluating the certainty of a hypothesis. Conditional independence is usually formulated in terms ofconditional probability, as a special case where the probability of the hypothesis given the uninfo... | https://en.wikipedia.org/wiki/Conditional_independence |
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