MACHINE LEARNING. TÉCNICAS DE APRENDIZAJE SUPERVISADO A TRAVÉS DE R PDF Download

Author: Cesar Perez Lopez
Publisher: CESAR PEREZ
ISBN: 1716967090
Category : Computers
Languages : en
Pages : 205

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Book Description
En este libro se desarrollarán técnicas de aprendizaje supervisado relativas a regresión. Más concretamente, se profundizará en los modelos lineales de regresión múltiple con toda su problemática de identificación, estimación y diagnosis. Se hace especial hincapié en el tratamiento de la multicolinealidad a través de la Ridge Regression (regresión en cadena) y el método PLS de los mínimos cuadrados parciales. Se dedica una parcela importante del contenido a los modelos de variable dependiente limitada y recuento, con especial mención a los modelos Logit y Probit. Por último se tratan también los modelos predictivos del análisis de la varianza y la covarianza.

Author: Rafael Valencia-García
Publisher: Springer Nature
ISBN: 3030620158
Category : Computers
Languages : en
Pages : 230

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Book Description
This book constitutes the proceedings of the 6th International Conference on Technologies and Innovation, CITI 2020, held in Guayaquil, Ecuador, in November-December 2020. The 16 full papers presented in this volume were carefully reviewed and selected from 41 submissions. They are organized in topical sections named: semantic technologies and machine learning; ICT for agronomy and environment; mobile and collaborative technologies.

Author: Mohammed Serrhini
Publisher: Springer Nature
ISBN: 3030367789
Category : Technology & Engineering
Languages : en
Pages : 659

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Book Description
This book provides glimpses into contemporary research in information systems & technology, learning, artificial intelligence (AI), machine learning, and security and how it applies to the real world, but the ideas presented also span the domains of telehealth, computer vision, the role and use of mobile devices, brain–computer interfaces, virtual reality, language and image processing and big data analytics and applications. Great research arises from asking pertinent research questions. This book reveals some of the authors’ “beautiful questions” and how they develop the subsequent “what if” and “how” questions, offering readers food for thought and whetting their appetite for further research by the same authors.

Author: Brindha Priyadarshini Jeyaraman
Publisher: Packt Publishing Ltd
ISBN: 1838552847
Category : Computers
Languages : en
Pages : 416

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Book Description
Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems Key FeaturesGain a comprehensive overview of different machine learning techniquesExplore various methods for selecting a particular algorithmImplement a machine learning project from problem definition through to the final modelBook Description With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it. What you will learnDefine a problem that can be solved by training a machine learning modelObtain, verify and clean data before transforming it into the correct format for usePerform exploratory analysis and extract features from dataBuild models for neural net, linear and non-linear regression, classification, and clusteringEvaluate the performance of a model with the right metricsImplement a classification problem using the neural net packageEmploy a decision tree using the random forest libraryWho this book is for If you are a data analyst, data scientist, or a business analyst who wants to understand the process of machine learning and apply it to a real dataset using R, this book is just what you need. Data scientists who use Python and want to implement their machine learning solutions using R will also find this book very useful. The book will also enable novice programmers to start their journey in data science. Basic knowledge of any programming language is all you need to get started.

Author: José Bravo
Publisher: Springer Nature
ISBN: 3031213335
Category : Computers
Languages : en
Pages : 1140

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Book Description
This book reports a set of novel research initiatives on ambient intelligence and ubiquitous computing that help researchers and practitioners identify recent advances, as well as the frontiers in these study domains. During the last two decades, both study areas have gained great interest in industry and academia due to the benefits of using smart solutions in various application domains, such as health care, ambient-assisted living, personal security and privacy, citizen participation, provision of urban services, and precision agriculture and farming. The articles included in this book report solutions and provide empirical results on their suitability to address problems and opportunities in these application domains. The articles also include discussions on how the proposals and their evaluation results inform the design of the next generation of ubiquitous and smart systems. Researchers, practitioners, and graduate students take advantage of this knowledge to address innovation and engineering aspects of smart and ubiquitous solutions for the next decade.

Author: Miguel Botto-Tobar
Publisher: Springer Nature
ISBN: 3030680800
Category : Technology & Engineering
Languages : en
Pages : 489

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Book Description
This book constitutes the proceedings of the XV Multidisciplinary International Congress on Science and Technology (CIT 2020), held in Quito, Ecuador, on 26–30 October 2020, proudly organized by Universidad de las Fuerzas Armadas ESPE in collaboration with GDEON. CIT is an international event with a multidisciplinary approach that promotes the dissemination of advances in Science and Technology research through the presentation of keynote conferences. In CIT, theoretical, technical, or application works that are research products are presented to discuss and debate ideas, experiences, and challenges. Presenting high-quality, peer-reviewed papers, the book discusses the following topics: Artificial Intelligence Computational Modeling Data Communications Defense Engineering Innovation, Technology, and Society Managing Technology & Sustained Innovation, and Business Development Modern Vehicle Technology Security and Cryptography Software Engineering

Author: Barlin Orlando Olivares Campos
Publisher: Springer Nature
ISBN: 3031344758
Category : Technology & Engineering
Languages : en
Pages : 194

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Book Description
This book presents an important contribution to the knowledge of the banana soils of Venezuela. Banana, the edible fruit of Musaceae, is a staple food for more than 400 million people worldwide due to their nutritional and energy attributes. This makes Musaceae a crop of worldwide relevance, particularly in tropical regions, highlighting the impact of improved Musaceae cropping systems in the current efforts worldwide oriented towards a new agricultural revolution based on sustainable intensification. To achieve this, better practices for food production based on scientific and technical research capable to consider the complexity and variability within the agri-food sector are necessary. The research presented in this book is oriented towards providing answers to the causes of two aspects considered of high relevance for banana production, both affecting productivity and sustainability, always addressed for the Venezuelan conditions, one of the world’s largest producing countries: 1- The impact of phytosanitary risks related to Fusarium Wilt (FW) and the influence of the soil on the incidence of Banana Wilt (BW); and 2- An observed trend towards loss of productivity and decline of soil quality in some commercial farms of Aragua and Trujillo states in Venezuela. The book presents a pioneering study in the application of categorical regression (CATREG) in the characterization of banana soils and analyses soil morphological variables, such as texture, dry consistency, biological activity, reaction to HCl, and type of structure to characterize soil productivity for growing healthy bananas. It develops, for the first time in Venezuela, risk analysis and climatic suitability maps for lethal wilt disease in bananas caused by Fusarium oxysporum f. sp. cubense (Foc) Tropical Race 4 (TR4) (syn. Fusarium odoratissimum). Innovative machine learning techniques are used to predict BW so that future crop development can be done more efficiently and sustainably.

Author: George Kyriakides
Publisher: Packt Publishing Ltd
ISBN: 178961788X
Category : Computers
Languages : en
Pages : 284

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Book Description
Combine popular machine learning techniques to create ensemble models using Python Key FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook Description Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you'll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios. What you will learnImplement ensemble methods to generate models with high accuracyOvercome challenges such as bias and varianceExplore machine learning algorithms to evaluate model performanceUnderstand how to construct, evaluate, and apply ensemble modelsAnalyze tweets in real time using Twitter's streaming APIUse Keras to build an ensemble of neural networks for the MovieLens datasetWho this book is for This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book.

Author: Miguel Botto-Tobar
Publisher: Springer Nature
ISBN: 3030715035
Category : Computers
Languages : en
Pages : 713

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Book Description
This volume constitutes the refereed proceedings of the Second International Conference on Applied Technologies, ICAT 2020, held in Quito, Ecuador, in December 2020. Due to the COVID-19 pandemic the conference was held online. The 53 papers were carefully reviewed and selected from 145 submissions. The papers are organized according to the following topics: communication; computing; e-government and e-participation; e-learning; electronics; intelligent systems; machine vision; security; technology trends.

Author: Lorna Uden
Publisher:
ISBN: 9783030207991
Category : Artificial intelligence
Languages : en
Pages : 488

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Book Description
This book constitutes the refereed proceedings of the 8th International Workshop on Learning Technology for Education Challenges, LTEC 2019, held in Zamora, Spain, in July 2019. The 41 revised full papers presented were carefully reviewed and selected from 83 submissions. The papers are organized in the following topical sections: learning technolgies; learning tools and environment; e-learning and MOOCs; learning practices; social media learning tools; machine learning and evaluation support programs. LTEC 2019 examines how these technologies and pedagogical advances can be used to change the way teachers teach and students learn, while giving special emphasis to the pedagogically effective ways we can harness these new technologies in education.