Index (search engine) Popular engines focus on the full-text indexing of online, natural language documents.[1] Media types such as video and audio[2] and graphics[3] are also searchable. Meta search engines reuse the indices of other services and do not store a local index, whereas cache-based search engines permanently store the index along with the corpus. Unlike full-text indices, partial-text services restrict the depth indexed to reduce index size. Larger services typically perform indexing at a predetermined time interval due to the required time and processing costs, while agent-based search engines index in real time. Indexing[edit] The purpose of storing an index is to optimize speed and performance in finding relevant documents for a search query. Index design factors[edit] Major factors in designing a search engine's architecture include: Merge factors Storage techniques How to store the index data, that is, whether information should be data compressed or filtered. Index size Lookup speed Maintenance
Neural Network Applications An Artificial Neural Network is a network of many very simple processors ("units"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. The units operate only on their local data and on the inputs they receive via the connections. The design motivation is what distinguishes neural networks from other mathematical techniques: A neural network is a processing device, either an algorithm, or actual hardware, whose design was motivated by the design and functioning of human brains and components thereof. There are many different types of Neural Networks, each of which has different strengths particular to their applications. 2.0 Applications There are abundant materials, tutorials, references and disparate list of demos on the net. The applications featured here are: PS: For those who are only interested in source codes for Neural Networks
Business intelligence Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. The goal of BI is to allow for the easy interpretation of these large volumes of data. BI technologies provide historical, current and predictive views of business operations. BI can be used to support a wide range of business decisions ranging from operational to strategic. Components[edit] Business intelligence is made up of an increasing number of components including: History[edit] In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. Business intelligence as it is understood today is said to have evolved from the decision support systems (DSS) that began in the 1960s and developed throughout the mid-1980s. Data warehousing[edit]
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,
DTREG -- Predictive Modeling Software Data architecture In information technology, data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations.[1] Data is usually one of several architecture domains that form the pillars of an enterprise architecture or solution architecture.[2] Overview[edit] A data architecture should[neutrality is disputed] set data standards for all its data systems as a vision or a model of the eventual interactions between those data systems. Data integration, for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems. A data architecture, in part, describes the data structures used by a business and its computer applications software. Essential to realizing the target state, Data Architecture describes how data is processed, stored, and utilized in an information system. Technology drivers
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]
Support Vector Machines vs Artificial Neural Networks Home The development of ANNs followed a heuristic path, with applications and extensive experimentation preceding theory. In contrast, the development of SVMs involved sound theory first, then implementation and experiments. A significant advantage of SVMs is that whilst ANNs can suffer from multiple local minima, the solution to an SVM is global and unique. "They differ radically from comparable approaches such as neural networks: SVM training always finds a global minimum, and their simple geometric interpretation provides fertile ground for further investigation." "Most often Gaussian kernels are used, when the resulted SVM corresponds to an RBF network with Gaussian radial basis functions. "In problems when linear decision hyperplanes are no longer feasible (section 2.4.3), an input space is mapped into a feature space (the hidden layer in NN models), resulting in a nonlinear classifier." "SVMs have been developed in the reverse order to the development of neural networks (NNs).
XSLT XSLT (Extensible Stylesheet Language Transformations) is a language for transforming XML documents into other XML documents,[1] or other objects such as HTML for web pages, plain text or into XSL Formatting Objects which can then be converted to PDF, PostScript and PNG.[2] The original document is not changed; rather, a new document is created based on the content of an existing one.[3] Typically, input documents are XML files, but anything from which the processor can build an XQuery and XPath Data Model can be used, for example relational database tables, or geographical information systems.[1] XSLT is a Turing-complete language, meaning it can specify any computation that can be performed by a computer.[4][5] History[edit] Design and processing model[edit] Diagram of the basic elements and process flow of Extensible Stylesheet Language Transformations. Processor implementations[edit] Performance[edit] Most early XSLT processors were interpreters. XSLT and XPath[edit] XSLT media types[edit] <?
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
Uma Introdução às Redes Neurais Aqui você terá noções básicas de redes neurais, passando por seu histórico, topologias, suas aplicações, passos para se desenvolver aplicações utilizando conceitos de redes neurais, chegando até exemplos práticos desenvolvidos por empresas espalhadas pelo mundo todo e que podem ser visitadas pela internet. Se você deseja conhecer as referências bibliográficas utilizadas neste trabalho, siga este link. Cassia Yuri Tatibana Deisi Yuki Kaetsu Índice: Resumo desta Página Uma Introdução às Redes Neurais Histórico Neurocomputação Motivação A Rede Neural Artificial Classificação de Redes Neurais Artificiais Topologias Aprendizado da Rede Desenvolvimento de Aplicações Aplicações de Redes Neurais Por que utilizar redes neurais? Considerações Finais Links para outros sites Programas de simulação - Downloads Referências Bibliográficas Esta página se propõe a descrever os principais tópicos referentes à redes neurais, desde seu surgimento até propostas de implementação em inúmeras aplicações atuais. Neurocomputação 1.