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Pattern Recognition And Machine Learning at Meripustak

Pattern Recognition And Machine Learning by Massimo Buscema, Excelic Press

Books from same Author: Massimo Buscema

Books from same Publisher: Excelic Press

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  • General Information  
    Author(s)Massimo Buscema
    PublisherExcelic Press
    ISBN9781642241525
    Pages365
    BindingHardcover
    LanguageEnglish
    Publish YearJanuary 2019

    Description

    Excelic Press Pattern Recognition And Machine Learning by Massimo Buscema

    Pattern recognition is persistent to be one of the imperative research fields in computer science and electrical engineering. Plenty of new applications are rising, and consequently pattern analysis and synthesis turn into significant subfields in pattern recognition. In these days, giving a computer to carry out any task involve a set of specific instructions or the accomplishment of an algorithm that defines the rules that need to be followed. The present day computer system has no ability to learn from past experiences and hence cannot readily recover on the basis of past mistakes. Subsequently, giving a computer or instructing a computer controlled program to execute a task entail one to define an absolute and accurate algorithm for task and then program the algorithm into the computer. Research in machine learning is now converging from several sources and from artificial intelligent field. This book as the name suggests Pattern Recognition and Machine Learning is packed with the benefits of machine learning and pattern recognition techniques and research in machine learning. The book covers chapters that aim to realize the future abilities by presenting a variety of integrated research in various scientific and engineering fields such as perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and also will be of helpful for graduate students, researchers, and practicing engineers working in the field of machine vision and computer science and engineering.



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