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MACHINE LEARNING con MATLAB. TÉCNICAS DE APRENDIZAJE NO SUPERVISADO: Clasificación

MACHINE LEARNING con MATLAB. TÉCNICAS DE APRENDIZAJE NO SUPERVISADO: Clasificación

          
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About the Book

La disponibilidad de grandes volúmenes de datos y el uso generalizado de instrumentos informáticos ha transformado la investigación y el análisis de datos, orientándolos hacia ciertas técnicas especializadas englobadas bajo el nombre genérico de Analytics que incluye el Análisis Multivariado de Datos (MDA), la Minería de Datos, Machine Learning y otras técnicas de Inteligencia Empresarial. El Machine Learning puede definirse como un proceso de descubrimiento de relaciones, patrones y tendencias nuevas y significativas al examinar grandes cantidades de datos. Las técnicas de minería de datos persiguen el descubrimiento automático de los conocimientos contenidos en la información almacenada de manera ordenada en grandes bases de datos. Estas técnicas tienen por objeto descubrir pautas, perfiles y tendencias mediante el análisis de datos utilizando técnicas estadísticas avanzadas de análisis multivariado de datos. El Machine Learning utiliza dos tipos de técnicas: las técnicas de aprendizaje supervisado o predictivas, que capacitan a un modelo sobre datos de entrada y salida conocidos para que pueda predecir los resultados futuros, y las técnicas de aprendizaje no supervisado o descriptivas, que encuentran patrones ocultos o estructuras intrínsecas en los datos de entrada. Las técnicas de aprendizaje no supervisado o descriptivas encuentran patrones ocultos o estructuras intrínsecas en los datos. La agrupación o clúster es la técnica descriptiva más común. Se utiliza para el análisis exploratorio de datos para encontrar patrones ocultos o agrupaciones en los datos. Otras técnicas no supervisadas de clasificcaión son las redes neuronales, el vecino más cercano kNN, las cadenas de Markov, los modelos de mezcla gausianos y el reconocimeinto de patrones. Este libro desarrolla técnicas descriptivas de clasificación a través de ejemplos resueltos con MATLAB.


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Product Details
  • ISBN-13: 9798648491830
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 229 mm
  • No of Pages: 312
  • Spine Width: 18 mm
  • Width: 152 mm
  • ISBN-10: 8648491835
  • Publisher Date: 25 May 2020
  • Binding: Paperback
  • Language: Spanish
  • Returnable: N
  • Weight: 458 gr


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