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Over 90 hands-on recipes for quickly preparing LaTeX documents to solve various challenging tasks Book Description LaTeX is both a word processor and a markup language. It's pretty simple really, which is why its unsurprising that its so popular within the research community - whether you're a scientist or statistician, with LaTeX, you'll find it easy to present and communicate your work - it gives you full control of the documents you create, no matter how complex they might be. With a huge range of customizable templates and supporting packages available, there's plenty to explore - so you can make LaTeX work for you. LaTeX Cookbook shows you how to get the most from LaTeX - so you can focus on what's important. Inside, you'll find everything from different templates and document types, samples for fine-tuning text design, guidance through embedding images, as well as drawing tables in LaTeX. With essential components to the modern research document included, such as creating a bibliography, glossary, and index, you can be confident that your document is clean and clear. You will also learn how to create graphics within LaTeX, and find vital tips to help you build diagrams with minimum fuss. If that isn't enough, the book also shows you how to use new engines XeTeX and LuaTeX which give LaTeX further functionality so you can handle any complexity with ease and elegance. If you're worried about presenting or communicating ideas and research for maximum impact, look no further than this book - it's sure to increase your productivity.

Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring data sets, as well as, for building predictive models. The main parts of the book include: Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. Model validation and evaluation techniques for measuring the performance of a predictive model. Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: Covers machine learning algorithm and implementation Key mathematical concepts are presented Short, self-contained chapters with practical examples. At the end of each chapter, we present R lab sections in which we systematically work through applications of the various methods discussed in that chapter.

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