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Related concepts (36)
Deep web
The deep web, invisible web, or hidden web are parts of the World Wide Web whose contents are not indexed by standard web search-engine programs. This is in contrast to the "surface web", which is accessible to anyone using the Internet. Computer scientist Michael K. Bergman is credited with inventing the term in 2001 as a search-indexing term. Deep web sites can be accessed by a direct URL or IP address, but may require entering a password or other security information to access actual content.
Web browsing history
Web browsing history refers to the list of web pages a user has visited, as well as associated metadata such as page title and time of visit. It is usually stored locally by web browsers in order to provide the user with a history list to go back to previously visited pages. It can reflect the user's interests, needs, and browsing habits. All major browsers have a private browsing mode in which browsing history is not recorded. This is to protect against browsing history being collected by third parties for targeted advertising or other purposes.
Outer measure
In the mathematical field of measure theory, an outer measure or exterior measure is a function defined on all subsets of a given set with values in the extended real numbers satisfying some additional technical conditions. The theory of outer measures was first introduced by Constantin Carathéodory to provide an abstract basis for the theory of measurable sets and countably additive measures.
Web design
Web design encompasses many different skills and disciplines in the production and maintenance of websites. The different areas of web design include web graphic design; user interface design (UI design); authoring, including standardised code and proprietary software; user experience design (UX design); and search engine optimization. Often many individuals will work in teams covering different aspects of the design process, although some designers will cover them all.
Machine learning
Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Brave (web browser)
Brave is a free and open-source web browser developed by Brave Software, Inc. based on the Chromium web browser. Brave is a privacy-focused browser, which automatically blocks some advertisements and website trackers in its default settings. Users can turn on optional ads that reward them for their attention in the form of Basic Attention Tokens (BAT), which can be used as a cryptocurrency or to make payments to registered websites and content creators. Brave Software's headquarters are in San Francisco, California.
Signed measure
In mathematics, signed measure is a generalization of the concept of (positive) measure by allowing the set function to take negative values. There are two slightly different concepts of a signed measure, depending on whether or not one allows it to take infinite values. Signed measures are usually only allowed to take finite real values, while some textbooks allow them to take infinite values. To avoid confusion, this article will call these two cases "finite signed measures" and "extended signed measures".
Web 2.0
Web 2.0 (also known as participative (or participatory) web and social web) refers to websites that emphasize user-generated content, ease of use, participatory culture and interoperability (i.e., compatibility with other products, systems, and devices) for end users. The term was coined by Darcy DiNucci in 1999 and later popularized by Tim O'Reilly and Dale Dougherty at the first Web 2.0 Conference in 2004.
Statistical classification
In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.
Multiclass classification
In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.