|with Applications in R|
|An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.|
|What To Do When Words Don’t Work|
|Ever been to so many meetings that you couldn’t get your work done? Ever fallen asleep during a bulletpoint presentation? Ever watched the news and ended up knowing less? Welcome to the land of Blah Blah Blah.
• The Problem: We talk so much that we don’t think very well. Powerful as words are, we fool ourselves when we think our words alone can detect, describe, and defuse the multifaceted problems of today. They can’t-and that’s bad, because words have become our default thinking tool.
• The Solution: This book offers a way out of blah-blah-blah. It’s called ‘Vivid Thinking.’
In Dan Roam’s first acclaimed book, The Back of the Napkin, he taught readers how to solve problems and sell ideas by drawing simple pictures. Now he proves that Vivid Thinking is even more powerful. This technique combines our verbal and visual minds so that we can think and learn more quickly, teach and inspire our colleagues, and enjoy and share ideas in a whole new way.
• The Destination: No more blah-blah-blah. Through Vivid Thinking, we can make the most complicated subjects suddenly crystal clear. Whether trying to understand a Harvard Business School class, or what went down in the Conan versus Leno battle for late-night TV, or what Einstein thought about relativity, Vivid Thinking provides a way to clarify anything.
Through dozens of guided examples, Roam proves that anyone can apply this systematic approach, from leftbrain types who hate to draw to right-brainers who hate to write. This isn’t just a book about improving communications, presentations, and ideation; it’s about removing the blah-blah- blah from your life for good.
|The technique of data fusion has been used extensively in information retrieval due to the complexity and diversity of tasks involved such as web and social networks, legal, enterprise, and many others. This book presents both a theoretical and empirical approach to data fusion. Several typical data fusion algorithms are discussed, analyzed and evaluated. A reader will find answers to the following questions, among others:
• What are the key factors that affect the performance of data fusion algorithms significantly?
• What conditions are favorable to data fusion algorithms?
• CombSum and CombMNZ, which one is better? and why?
• What is the rationale of using the linear combination method?
• How can the best fusion option be found under any given circumstances?
|Visualizing a Million|
|This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases, or large in numbers of variables, or large in both. All ideas are illustrated with displays from analyses of real datasets and the importance of interpreting displays effectively is emphasized. Graphics should be drawn to convey information and the book includes many insightful examples. New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. The book is accessible to readers with some experience of drawing statistical graphics.|
|Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data|
|Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured factor models that extend from the Gaussian assumption to copulas. It also discusses other multivariate constructions and parametric copula families that have different tail properties and presents extensive material on dependence and tail properties to assist in copula model selection. The author shows how numerical methods and algorithms for inference and simulation are important in high-dimensional copula applications. He presents the algorithms as pseudocode, illustrating their implementation for high-dimensional copula models. He also incorporates results to determine dependence and tail properties of multivariate distributions for future constructions of copula models.|
|Improving Accuracy Through Combining Predictions|
|Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges — from investment timing to drug discovery, and fraud detection to recommendation systems — where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization — today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods — bagging, random forests, and boosting — to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners — especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques.|
|Deploy cutting-edge sentiment analysis techniques to real-world social media data using R|
|There’s probably no better place to gain behavioral insights than through social media, but analyzing the mass of data is often difficult. With this book you’ll learn to employ the latest techniques and processes using R.
• Learn how to face the challenges of analyzing social media data
• Get hands-on experience with the most common, up-to-date sentiment analysis tools and apply them to data collected from social media websites through a series of in-depth case studies, which includes how to mine Twitter data
• A focused guide to help you achieve practical results when interpreting social media data
What you will learn from this book
• Learn the basics of R and all the data types
• Explore the vast expanse of social science research
• Discover more about data potential, the pitfalls, and inferential gotchas
• Gain an insight into the concepts of supervised and unsupervised learning
• Familiarize yourself with visualization and some cognitive pitfalls
• Delve into exploratory data analysis
• Understand the minute details of sentiment analysis
|Concepts and Methods|
|The Handbook of Computational Statistics: Concepts and Methodology is divided into four parts. It begins with an overview over the field of Computational Statistics. The second part presents several topics in the supporting field of statistical computing. Emphasis is placed on the need of fast and accurate numerical algorithms and it discusses some of the basic methodologies for transformation, data base handling and graphics treatment. The third part focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Finally a set of selected applications like Bioinformatics, Medical Imaging, Finance and Network Intrusion Detection highlight the usefulness of computational statistics.|
|Building Smart Web 2.0 Applications|
|Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you’ve found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general — all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:
• Collaborative filtering techniques that enable online retailers to recommend products or media
• Methods of clustering to detect groups of similar items in a large dataset
• Search engine features — crawlers, indexers, query engines, and the PageRank algorithm
• Optimization algorithms that search millions of possible solutions to a problem and choose the best one
• Bayesian filtering, used in spam filters for classifying documents based on word types and other features
• Using decision trees not only to make predictions, but to model the way decisions are made
• Predicting numerical values rather than classifications to build price models
• Support vector machines to match people in online dating sites
• Non-negative matrix factorization to find the independent features in a dataset
• Evolving intelligence for problem solving — how a computer develops its skill by improving its own code the more it plays a game
Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. ‘Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details.’ — Dan Russell, Google ‘Toby’s book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths.’ — Tim Wolters, CTO, Collective Intellect
|Practical Machine Learning Tools and Techniques|
|Data Mining: Practical Machine Learning Tools and Techniques’ offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. It provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects. It offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods. It includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization.|