Mineria de Datos. Redes Neuronales Y Rboles de Decisin / Data Mining. Neural Networks and Decision Trees
Mineria de Datos. Redes Neuronales Y Rboles de Decisin / Data Mining. Neural Networks and Decision Trees

Author: Cesar Perez Lopez

Publisher: CreateSpace

Published: 2013-11

Total Pages: 196

ISBN-13: 9781493768400

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Este libro profundiza en dos de las técnicas más habituales utilizadas en minería de datos, como son las redes neuronales y los árboles de decisión. El contenido se aborda de una forma sencilla y fácil de entender a través de una de las soluciones de software más comunes de entre las existentes en el mercado, en concreto, SAS ENTERPRISE MINER. Se persigue como finalidad inicial clarificar las aplicaciones relativas a métodos tradicionalmente calificados como difíciles u opacos. Se busca presentar las aplicaciones en la minería de datos sin necesidad de manejar desarrollos matemáticos elevados ni algoritmos teóricos complicados, que es la razón más común de las dificultades en la comprensión y aplicación de esta materia.Hoy en día se utiliza la minería de datos en diferentes campos de la ciencia. Cabe destacar las aplicaciones financieras y en banca, en análisis de mercados y comercio, en seguros y salud privada, en educación, en procesos industriales, en medicina, en biología y bioingeniería, en telecomunicaciones y en muchas otras áreas. Lo esencial para empezar a trabajar en minería de datos, sea cual sea el campo en que se aplique, es la comprensión de los propios conceptos, tarea que no exige ni mucho menos el dominio de aparato científico que conlleva la materia. Posteriormente, cuando ya sea necesaria la operatoria avanzada, los programas de ordenador permiten obtener los resultados sin necesidad de descifrar el desarrollo matemático de los algoritmos que están debajo de los procedimientos.

MINERIA de DATOS a Través de SAS ENTERPRISE MINER. Redes Neuronales y Arboles de Decisión
MINERIA de DATOS a Través de SAS ENTERPRISE MINER. Redes Neuronales y Arboles de Decisión

Author: María Marqués

Publisher: CreateSpace

Published: 2014-12-03

Total Pages: 192

ISBN-13: 9781505346374

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Para los investigadores, la extracción del conocimiento a partir de grandes cantidades de datos es una de las áreas de trabajo que suponen un gran reto, al tener que buscar una nueva manera de pensar, diseñar, e implementarlas metodologías existentes relativas el análisis de los datos. Por otro lado, una pléyade de investigadores de múltiples áreas aplicadas, como la biología, medicina, economía, etc. han descubierto el enorme potencial que supone las aportaciones teóricas en Minería de Datos para resolver con éxito problemas reales que anteriormente eran tratados de forma simple. Ello ha supuesto que actualmente el Data Mining sea una de las áreas profesionales y de investigación más activas y excitantes.De las múltiples definiciones más o menos equivalentes que existen de Data Mining, la definición que hace el Instituto SAS recoge con acierto la idea que subyace a este concepto. SAS define el concepto de Data Mining como el proceso de Seleccionar (Selecting), Explorar (Exploring), Modificar (Modifying), Modelar (Modeling) y Valorar (Assessment) grandes cantidades de datos con el objetivo de descubrir patrones desconocidos que puedan ser utilizados como ventaja. Este libro trata las técnicas que se utilizan en todas las fases del proceso de Minería de Datos haciendo especial hincapié en las Redes Neuronales y los Árboles de Decisión, que constituyen Técnicas Predictivas avanzadas de precisión óptima con gran aplicación práctica. Todas las técnicas se desarrollan a través de ejemplos.

Data Mining Methods and Applications
Data Mining Methods and Applications

Author: Kenneth D. Lawrence

Publisher: CRC Press

Published: 2007-12-22

Total Pages: 334

ISBN-13: 1420013734

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With today's information explosion, many organizations are now able to access a wealth of valuable data. Unfortunately, most of these organizations find they are ill-equipped to organize this information, let alone put it to work for them. Gain a Competitive Advantage Employ data mining in research and forecasting Build models with data management

Data Mining with Neural Networks
Data Mining with Neural Networks

Author: Joseph P. Bigus

Publisher: McGraw-Hill Companies

Published: 1996

Total Pages: 248

ISBN-13:

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readers will find concrete implementation strategies, reinforced with real-world business examples and a minimum of formulas, and case studies drawn from a broad range of industries. The book illustrates the popular data mining functions of classification, clustering, modeling, and time-series forecasting--through examples developed using the IBM Neural Network Utility.

Intelligent Data Mining in Law Enforcement Analytics
Intelligent Data Mining in Law Enforcement Analytics

Author: Paolo Massimo Buscema

Publisher: Springer Science & Business Media

Published: 2012-11-28

Total Pages: 522

ISBN-13: 9400749147

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This book provides a thorough summary of the means currently available to the investigators of Artificial Intelligence for making criminal behavior (both individual and collective) foreseeable, and for assisting their investigative capacities. The volume provides chapters on the introduction of artificial intelligence and machine learning suitable for an upper level undergraduate with exposure to mathematics and some programming skill or a graduate course. It also brings the latest research in Artificial Intelligence to life with its chapters on fascinating applications in the area of law enforcement, though much is also being accomplished in the fields of medicine and bioengineering. Individuals with a background in Artificial Intelligence will find the opening chapters to be an excellent refresher but the greatest excitement will likely be the law enforcement examples, for little has been done in that area. The editors have chosen to shine a bright light on law enforcement analytics utilizing artificial neural network technology to encourage other researchers to become involved in this very important and timely field of study.

Neural Networks and Statistical Learning
Neural Networks and Statistical Learning

Author: Ke-Lin Du

Publisher: Springer Nature

Published: 2019-09-12

Total Pages: 988

ISBN-13: 1447174526

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This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

Machine Learning and Its Applications
Machine Learning and Its Applications

Author: Georgios Paliouras

Publisher: Springer

Published: 2003-06-29

Total Pages: 334

ISBN-13: 3540446737

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In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.

Introduction to Neural Networks and Data Mining for Business Applications
Introduction to Neural Networks and Data Mining for Business Applications

Author: Kate A. Smith

Publisher:

Published: 1999

Total Pages: 155

ISBN-13: 9781864910049

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Neural networks are a hot topic in the business community today. Also marketed as intelligent techniques, business intelligence and data mining, many businesses are now realising the potential of neural networks to give them a competitive edge. Nevertheless most neural network books are written by electrical engineers for electrical engineers, with a high level of mathematics. Those few books aimed at the business community invariably focus exclusively on financial prediction. Consequently, Introduction to Neural Networks and Data Mining for Business Applications is a ground breaking text. With a minimum of mathematics, it shows the potential of neural networks to unlock hidden information in data of various industries including retail, marketing, insurance, telecommunications, banking and finance, and operations management. The book covers the development of neural network research and its impact on business; the early neural Perceptron model and its limitations; backpropagation, the most commonly used learning paradigm in business applications; self-organisation; and adaptive resonance theory. Data mining is then covered including the purpose, methodology, and concepts of directed and undirected knowledge discovery. Other intelligent techniques often used in conjunction with neural networks are also covered, including genetic algorithms, fuzzy logic, and expert systems. The text concludes with a discussion of the future of neural networks research and applications. Extensive business case studies are used throughout the text to demonstrate techniques.

Neural Networks with SAS Enterprise Miner
Neural Networks with SAS Enterprise Miner

Author: Scientific Books

Publisher: CreateSpace

Published: 2015-06-22

Total Pages: 292

ISBN-13: 9781514652282

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SAS Institute implements data mining in Enterprise Miner software, which will be used in this book focused in Neural Networks models. SAS Institute defines the concept of Data Mining as the process of selecting (Selecting), explore (Exploring), modify (Modifying), modeling (Modeling) and rating (Assessment) large amounts of data with the aim of uncovering unknown patterns which can be used as a comparative advantage with respect to competitors. This process is summarized with the acronym SEMMA which are the initials of the 5 phases which comprise the process of Data Mining according to SAS Institute.

Deep Neural Network Applications
Deep Neural Network Applications

Author: Hasmik Osipyan

Publisher: CRC Press

Published: 2022-04-28

Total Pages: 158

ISBN-13: 0429556209

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The world is on the verge of fully ushering in the fourth industrial revolution, of which artificial intelligence (AI) is the most important new general-purpose technology. Like the steam engine that led to the widespread commercial use of driving machineries in the industries during the first industrial revolution; the internal combustion engine that gave rise to cars, trucks, and airplanes; electricity that caused the second industrial revolution through the discovery of direct and alternating current; and the Internet, which led to the emergence of the information age, AI is a transformational technology. It will cause a paradigm shift in the way’s problems are solved in every aspect of our lives, and, from it, innovative technologies will emerge. AI is the theory and development of machines that can imitate human intelligence in tasks such as visual perception, speech recognition, decision-making, and human language translation. This book provides a complete overview on the deep learning applications and deep neural network architectures. It also gives an overview on most advanced future-looking fundamental research in deep learning application in artificial intelligence. Research overview includes reasoning approaches, problem solving, knowledge representation, planning, learning, natural language processing, perception, motion and manipulation, social intelligence and creativity. It will allow the reader to gain a deep and broad knowledge of the latest engineering technologies of AI and Deep Learning and is an excellent resource for academic research and industry applications.