Predictive analytics
Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.[1][2] In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.[3] Predictive analytics is used in actuarial science,[4] marketing,[5] financial services,[6] insurance, telecommunications,[7] retail,[8] travel,[9] healthcare,[10] pharmaceuticals[11] and other fields. One of the most well known applications is credit scoring,[1] which is used throughout financial services. Definition[edit] Types[edit] Predictive models[edit] Descriptive models[edit] Decision models[edit] Applications[edit] Collection analytics[edit]
List of social bookmarking websites
Defunct sites[edit] See also[edit] Notes and references[edit]
Google Guide Quick Reference: Google Advanced Operators (Cheat Sheet)
The following table lists the search operators that work with each Google search service. Click on an operator to jump to its description — or, to read about all of the operators, simply scroll down and read all of this page. The following is an alphabetical list of the search operators. Each entry typically includes the syntax, the capabilities, and an example. allinanchor: If you start your query with allinanchor:, Google restricts results to pages containing all query terms you specify in the anchor text on links to the page. Anchor text is the text on a page that is linked to another web page or a different place on the current page. allintext: If you start your query with allintext:, Google restricts results to those containing all the query terms you specify in the text of the page. allintitle: If you start your query with allintitle:, Google restricts results to those containing all the query terms you specify in the title. allinurl: In URLs, words are often run together. author: ext:
Optimization (mathematics)
In mathematics, computer science, or management science, mathematical optimization (alternatively, optimization or mathematical programming) is the selection of a best element (with regard to some criteria) from some set of available alternatives.[1] Optimization problems[edit] An optimization problem can be represented in the following way: Sought: an element x0 in A such that f(x0) ≤ f(x) for all x in A ("minimization") or such that f(x0) ≥ f(x) for all x in A ("maximization"). Such a formulation is called an optimization problem or a mathematical programming problem (a term not directly related to computer programming, but still in use for example in linear programming – see History below). Many real-world and theoretical problems may be modeled in this general framework. By convention, the standard form of an optimization problem is stated in terms of minimization. the expression Notation[edit] Optimization problems are often expressed with special notation. . , occurring at Similarly,
Web crawler
Not to be confused with offline reader. For the search engine of the same name, see WebCrawler. Crawlers can validate hyperlinks and HTML code. They can also be used for web scraping (see also data-driven programming). Overview[edit] A Web crawler starts with a list of URLs to visit, called the seeds. The large volume implies that the crawler can only download a limited number of the Web pages within a given time, so it needs to prioritize its downloads. The number of possible URLs crawled being generated by server-side software has also made it difficult for web crawlers to avoid retrieving duplicate content. Crawling policy[edit] The behavior of a Web crawler is the outcome of a combination of policies:[6] a selection policy that states which pages to download,a re-visit policy that states when to check for changes to the pages,a politeness policy that states how to avoid overloading Web sites, anda parallelization policy that states how to coordinate distributed web crawlers.
Search Engine Directory
Monte Carlo method
Monte Carlo methods (or Monte Carlo experiments) are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results; typically one runs simulations many times over in order to obtain the distribution of an unknown probabilistic entity. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to obtain a closed-form expression, or infeasible to apply a deterministic algorithm. Monte Carlo methods are mainly used in three distinct problem classes: optimization, numerical integration and generation of draws from a probability distribution. The modern version of the Monte Carlo method was invented in the late 1940s by Stanislaw Ulam, while he was working on nuclear weapons projects at the Los Alamos National Laboratory. Introduction[edit] Monte Carlo method applied to approximating the value of π. Monte Carlo methods vary, but tend to follow a particular pattern: History[edit] Definitions[edit]
Crawl
From Wikipedia, the free encyclopedia Crawl or crawling may refer to: Music[edit] Television and film[edit] See also[edit]
wow
Multidisciplinary design optimization - Wikipedia, the free ency
MDO allows designers to incorporate all relevant disciplines simultaneously. The optimum of the simultaneous problem is superior to the design found by optimizing each discipline sequentially, since it can exploit the interactions between the disciplines. However, including all disciplines simultaneously significantly increases the complexity of the problem. These techniques have been used in a number of fields, including automobile design, naval architecture, electronics, architecture, computers, and electricity distribution. However, the largest number of applications have been in the field of aerospace engineering, such as aircraft and spacecraft design. History[edit] Since 1990, the techniques have expanded to other industries. Origins in structural optimization[edit] Gradient-based methods[edit] There were two schools of structural optimization practitioners using gradient-based methods during the 1960s and 1970s: optimality criteria and mathematical programming. Constraints[edit] find
