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Generative Model
In statistical classification, two main approaches are called the generative approach and the discriminative approach. These compute classifiers by different approaches, differing in the degree of statistical modelling. Terminology is inconsistent, but three major types can be distinguished: # A generative model is a statistical model of the joint probability distribution P(X, Y) on a given observable variable ''X'' and target variable ''Y'';: "Generative classifiers learn a model of the joint probability, p(x, y), of the inputs ''x'' and the label ''y'', and make their predictions by using Bayes rules to calculate p(y\mid x), and then picking the most likely label ''y''. A generative model can be used to "generate" random instances ( outcomes) of an observation ''x''. # A discriminative model is a model of the conditional probability P(Y\mid X = x) of the target ''Y'', given an observation ''x''. It can be used to "discriminate" the value of the target variable ''Y'', given an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pleonasm
Pleonasm (; , ) is redundancy in linguistic expression, such as "black darkness", "burning fire", "the man he said", or "vibrating with motion". It is a manifestation of tautology by traditional rhetorical criteria. Pleonasm may also be used for emphasis, or because the phrase has become established in a certain form. Tautology and pleonasm are not consistently differentiated in literature. Usage Most often, ''pleonasm'' is understood to mean a word or phrase which is useless, clichéd, or repetitive, but a pleonasm can also be simply an unremarkable use of idiom. It can aid in achieving a specific linguistic effect, be it social, poetic or literary. Pleonasm sometimes serves the same function as rhetorical repetition—it can be used to reinforce an idea, contention or question, rendering writing clearer and easier to understand. Pleonasm can serve as a redundancy check; if a word is unknown, misunderstood, misheard, or if the medium of communication is poor—a static-fille ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Generative Adversarial Networks
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning. The core idea of a GAN is based on the "indirect" training through the discriminator, another neural network that can tell how ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Autoencoder
An autoencoder is a type of artificial neural network used to learn efficient codings of 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 learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction, to generate lower-dimensional embeddings for subsequent use by other machine learning algorithms. Variants exist which aim to make the learned representations assume useful properties. Examples are regularized autoencoders (''sparse'', ''denoising'' and ''contractive'' autoencoders), which are effective in learning representations for subsequent classification tasks, and ''variational'' autoencoders, which can be used as generative models. Autoencoders are applied to many problems, including facial recognition, feature detection, anomaly detection, and l ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Microsoft
Microsoft Corporation is an American multinational corporation and technology company, technology conglomerate headquartered in Redmond, Washington. Founded in 1975, the company became influential in the History of personal computers#The early 1980s and home computers, rise of personal computers through software like Windows, and the company has since expanded to Internet services, cloud computing, video gaming and other fields. Microsoft is the List of the largest software companies, largest software maker, one of the Trillion-dollar company, most valuable public U.S. companies, and one of the List of most valuable brands, most valuable brands globally. Microsoft was founded by Bill Gates and Paul Allen to develop and sell BASIC interpreters for the Altair 8800. It rose to dominate the personal computer operating system market with MS-DOS in the mid-1980s, followed by Windows. During the 41 years from 1980 to 2021 Microsoft released 9 versions of MS-DOS with a median frequen ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Deep Learning
Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be either supervised, semi-supervised or unsupervised. Some common deep learning network architectures include fully connected networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance fields. These architectures have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Classification (machine Learning)
When classification is performed by a computer, statistical methods are normally used to develop the algorithm. Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or ''features''. These properties may variously be categorical data, categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal data, ordinal (e.g. "large", "medium" or "small"), integer, integer-valued (e.g. the number of occurrences of a particular word in an email) or real number, real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity function, similarity or metric (mathematics), distance function. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function (mathematics), function, implemented by a classification algo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayes' Rule
Bayes' theorem (alternatively Bayes' law or Bayes' rule, after Thomas Bayes) gives a mathematical rule for inverting conditional probabilities, allowing one to find the probability of a cause given its effect. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to someone of a known age to be assessed more accurately by conditioning it relative to their age, rather than assuming that the person is typical of the population as a whole. Based on Bayes' law, both the prevalence of a disease in a given population and the error rate of an infectious disease test must be taken into account to evaluate the meaning of a positive test result and avoid the '' base-rate fallacy''. One of Bayes' theorem's many applications is Bayesian inference, an approach to statistical inference, where it is used to invert the probability of observations given a model configuration (i.e., the likelihood function) to obtain the probability o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Probability Distribution
In probability theory and statistics, a probability distribution is a Function (mathematics), function that gives the probabilities of occurrence of possible events for an Experiment (probability theory), experiment. It is a mathematical description of a Randomness, random phenomenon in terms of its sample space and the Probability, probabilities of Event (probability theory), events (subsets of the sample space). For instance, if is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of would take the value 0.5 (1 in 2 or 1/2) for , and 0.5 for (assuming that fair coin, the coin is fair). More commonly, probability distributions are used to compare the relative occurrence of many different random values. Probability distributions can be defined in different ways and for discrete or for continuous variables. Distributions with special properties or for especially important applications are given specific names. Introduction A prob ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Marginal Distribution
In probability theory and statistics, the marginal distribution of a subset of a collection of random variables is the probability distribution of the variables contained in the subset. It gives the probabilities of various values of the variables in the subset without reference to the values of the other variables. This contrasts with a conditional distribution, which gives the probabilities contingent upon the values of the other variables. Marginal variables are those variables in the subset of variables being retained. These concepts are "marginal" because they can be found by summing values in a table along rows or columns, and writing the sum in the margins of the table. The distribution of the marginal variables (the marginal distribution) is obtained by marginalizing (that is, focusing on the sums in the margin) over the distribution of the variables being discarded, and the discarded variables are said to have been marginalized out. The context here is that the theoreti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Target Function
Target may refer to: Warfare and shooting * Shooting target, used in marksmanship training and various shooting sports ** Bullseye (target), the goal one for which one aims in many of these sports ** Aiming point, in field artillery, fixed at a specific target * Targeting (warfare), lists various military targets * Color chart (or reference card), the reference target used in digital imaging for accurate color reproduction Places * Target, Allier, France * Target Lake, a lake in Minnesota Terms * Target market, marketing strategy ** Target audience, intended audience or readership of a publication, advertisement, or type of message * In mathematics, the target of a function is also called the codomain; more generally, a morphism has a target * Target (cricket), the total number of runs a team needs to win People * Target (rapper), stage name of Croatian hip-hop artist Nenad Šimun * DJ Target, stage name of English grime DJ Darren Joseph, member of Roll Deep ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
